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Cards issued by JP 42 00:02:16,600 --> 00:02:20,600 Speaker 3: Morgan Chase Bank NA Member FDIC subject to credit approval offer, 43 00:02:20,680 --> 00:02:22,480 Speaker 3: subject to change terms apply. 44 00:02:30,240 --> 00:02:32,720 Speaker 4: Andy Daniel, did you always want to be a paid physicist? 45 00:02:34,760 --> 00:02:36,800 Speaker 1: Definitely not. When I was a kid, I did not 46 00:02:36,880 --> 00:02:37,760 Speaker 1: want to be a physicist. 47 00:02:38,160 --> 00:02:40,640 Speaker 4: Really, you knew what it was, but you knew you 48 00:02:40,639 --> 00:02:41,720 Speaker 4: didn't want to be one. 49 00:02:42,200 --> 00:02:44,800 Speaker 1: I don't think I understood what a scientist was well enough. 50 00:02:44,800 --> 00:02:46,320 Speaker 1: But when I was a kid, I wanted to be 51 00:02:46,360 --> 00:02:49,040 Speaker 1: an explorer. I wanted to get on a ship and 52 00:02:49,280 --> 00:02:51,639 Speaker 1: find some new island and name it after myself. 53 00:02:51,800 --> 00:02:54,040 Speaker 4: You just about to get out of Los Alamos, said 54 00:02:54,040 --> 00:02:56,160 Speaker 4: the main purpose here. 55 00:02:56,560 --> 00:02:58,360 Speaker 1: Yeah, though you can't really take a ship out of 56 00:02:58,360 --> 00:03:00,760 Speaker 1: Los Alamos because it's landlocked. So there were some basic 57 00:03:00,800 --> 00:03:01,799 Speaker 1: problems in my thinking. 58 00:03:02,120 --> 00:03:04,000 Speaker 4: Well, you could take a train and then aship. But 59 00:03:04,040 --> 00:03:07,119 Speaker 4: don't they say everyone's a physicists, especially little kids. 60 00:03:08,080 --> 00:03:10,440 Speaker 1: Yeah. I think everybody is a scientist because they are 61 00:03:10,560 --> 00:03:13,760 Speaker 1: curious about the world. And in the end I discovered 62 00:03:13,840 --> 00:03:16,239 Speaker 1: that being a physicist it's kind of like being an explorer, 63 00:03:16,280 --> 00:03:19,000 Speaker 1: except instead of discovering new continents, we're trying to discover 64 00:03:19,360 --> 00:03:20,720 Speaker 1: new frontiers of knowledge. 65 00:03:21,880 --> 00:03:24,239 Speaker 4: Instead of surfing the waves out there and the sea, 66 00:03:24,360 --> 00:03:27,400 Speaker 4: you're surfing the couch. Mostly. 67 00:03:27,760 --> 00:03:30,840 Speaker 1: I'm clickly clacking my way to new shores of knowledge. 68 00:03:33,000 --> 00:03:34,560 Speaker 4: Just don't get scurvy on your couch. 69 00:03:35,800 --> 00:03:37,480 Speaker 1: I got a bowl of limes here next to. 70 00:03:37,440 --> 00:03:41,400 Speaker 4: Me, Okay, with the tequila and the margaritas. That's for 71 00:03:41,520 --> 00:03:47,160 Speaker 4: after work, After work work these days? What's the difference. 72 00:04:02,680 --> 00:04:05,240 Speaker 4: Hi'm Hori. I'm a cartoonist and the author of Oliver's 73 00:04:05,240 --> 00:04:06,200 Speaker 4: Great Big Universe. 74 00:04:06,400 --> 00:04:09,040 Speaker 1: Hi I'm Daniel. I'm a particle physicist and a professor 75 00:04:09,080 --> 00:04:11,440 Speaker 1: at UC Irvine, and I want to teach people to 76 00:04:11,560 --> 00:04:12,720 Speaker 1: think like a physicist. 77 00:04:12,760 --> 00:04:15,240 Speaker 4: Wait, I'm confused. If everyone's a physicist, aren't just teaching 78 00:04:15,280 --> 00:04:16,880 Speaker 4: people to think like humans? 79 00:04:19,520 --> 00:04:22,320 Speaker 1: Yeah, basically, I'm done. I can retire. It's after work time. 80 00:04:22,640 --> 00:04:23,680 Speaker 1: Where's margarita? 81 00:04:24,520 --> 00:04:25,920 Speaker 4: I know, let's get the shots going. 82 00:04:27,080 --> 00:04:30,000 Speaker 1: No, I think everybody does have curiosity, but you know, 83 00:04:30,040 --> 00:04:32,080 Speaker 1: it took us a while to figure out some tips 84 00:04:32,080 --> 00:04:36,120 Speaker 1: and some tricks to effectively extract knowledge from the universe 85 00:04:36,640 --> 00:04:38,880 Speaker 1: rather than just like you know, making up cute stories 86 00:04:38,920 --> 00:04:40,200 Speaker 1: to satisfy our curiosity. 87 00:04:40,600 --> 00:04:42,520 Speaker 4: Right, it probably took a while to get paid to 88 00:04:42,520 --> 00:04:43,039 Speaker 4: do it too. 89 00:04:43,279 --> 00:04:45,600 Speaker 1: Yeah, that's certainly true. A lot of the big names 90 00:04:45,600 --> 00:04:48,320 Speaker 1: in the history of science were men of leisure, you know, 91 00:04:48,920 --> 00:04:52,279 Speaker 1: operating on their trust funds or daddy's bank account. 92 00:04:53,000 --> 00:04:55,760 Speaker 4: Who do you think was the first professional physicist? 93 00:04:56,000 --> 00:04:58,719 Speaker 1: You know, science as a profession is not actually that old. 94 00:04:59,320 --> 00:05:02,120 Speaker 1: It's something like in the late eighteen hundreds that people 95 00:05:02,240 --> 00:05:05,600 Speaker 1: started to call themselves scientists and get paid to do it. 96 00:05:05,920 --> 00:05:07,880 Speaker 1: There are money to hire people to do this kind 97 00:05:07,920 --> 00:05:10,960 Speaker 1: of research. Until then, it was you know, natural philosophers 98 00:05:11,000 --> 00:05:13,080 Speaker 1: and people just sort of like curious poking around in 99 00:05:13,120 --> 00:05:16,800 Speaker 1: their own laboratories. Yeah, but scientists as a job is 100 00:05:16,839 --> 00:05:18,360 Speaker 1: not much more than like one hundred years old. 101 00:05:18,480 --> 00:05:21,080 Speaker 4: WHOA. So even the word science is relatively new. 102 00:05:21,480 --> 00:05:24,040 Speaker 1: Yeah, exactly. If you ask like Gauss or Newton or 103 00:05:24,120 --> 00:05:26,920 Speaker 1: leading It or Aristotle, they certainly would not call themselves 104 00:05:27,000 --> 00:05:29,080 Speaker 1: a scientist. That's a new word. 105 00:05:30,320 --> 00:05:33,839 Speaker 4: Or maybe they did it on purpose. They're like science, No, thanks, 106 00:05:35,360 --> 00:05:37,240 Speaker 4: it's a new fangled thing that all the kids are 107 00:05:37,279 --> 00:05:41,720 Speaker 4: talking about. I prefer to be a natural philosopher. But anyways, 108 00:05:41,720 --> 00:05:44,120 Speaker 4: welcome to our podcast. Daniel and Jorge explain the universe 109 00:05:44,160 --> 00:05:46,360 Speaker 4: a production of iHeartRadio. 110 00:05:45,800 --> 00:05:48,080 Speaker 1: In which we do our best to demonstrate what it's 111 00:05:48,200 --> 00:05:51,200 Speaker 1: like to think like a physicist. We take a physicist 112 00:05:51,279 --> 00:05:54,800 Speaker 1: approach to dismantling the whole universe, understanding all of its 113 00:05:54,839 --> 00:05:58,640 Speaker 1: little bits, building mental mathematical models to try to explain it, 114 00:05:58,920 --> 00:06:02,000 Speaker 1: asking questions of those models, and then wondering what does 115 00:06:02,040 --> 00:06:03,479 Speaker 1: it all mean anyway? 116 00:06:03,760 --> 00:06:06,240 Speaker 4: Yeah, because, as we talked about before, the universe belongs 117 00:06:06,240 --> 00:06:09,840 Speaker 4: to everyone, and asking questions is everyone's job, but a 118 00:06:09,880 --> 00:06:14,200 Speaker 4: few people get to do it as a career, get. 119 00:06:14,040 --> 00:06:17,400 Speaker 1: To Yes, exactly. It's definitely a treat and a privilege. 120 00:06:19,600 --> 00:06:22,080 Speaker 4: Well you get paid to do it, I guess, and 121 00:06:22,240 --> 00:06:24,440 Speaker 4: to do that, there's a certain mindset you have to have, 122 00:06:24,600 --> 00:06:26,400 Speaker 4: right in order to be part of the profession. 123 00:06:26,480 --> 00:06:28,800 Speaker 1: Yeah, there definitely is a way of thinking that's sort 124 00:06:28,839 --> 00:06:31,799 Speaker 1: of like a physicist way of thinking. And I see 125 00:06:31,800 --> 00:06:34,520 Speaker 1: this because people who are trained as physicists and then 126 00:06:34,560 --> 00:06:37,080 Speaker 1: go out into the world and work in other areas 127 00:06:37,520 --> 00:06:42,120 Speaker 1: chemistry or engineering or computer science still take with them 128 00:06:42,240 --> 00:06:45,760 Speaker 1: a certain mindset, a certain way of approaching problems, which 129 00:06:45,760 --> 00:06:48,360 Speaker 1: can be really really helpful and useful or also sometimes 130 00:06:48,400 --> 00:06:49,760 Speaker 1: frustrating for their colleagues. 131 00:06:50,560 --> 00:06:53,039 Speaker 4: Yeah, no, I can totally relate. I think that also 132 00:06:53,080 --> 00:06:55,160 Speaker 4: the same is for engineers. You know, anyone who studied 133 00:06:55,160 --> 00:06:57,760 Speaker 4: engineering definitely thinks like an engineer is trying to think 134 00:06:57,800 --> 00:07:00,640 Speaker 4: it a certain way and a certain mind. Said about 135 00:07:01,160 --> 00:07:02,719 Speaker 4: tackling problems for sure. 136 00:07:02,760 --> 00:07:06,719 Speaker 1: Yeah, absolutely take an engineering approach to cartooning. For example. 137 00:07:06,880 --> 00:07:08,760 Speaker 4: Yeah, whenever I draw a bridge, I mean I really 138 00:07:08,800 --> 00:07:13,200 Speaker 4: put some calculations behind it. Why to make sure it 139 00:07:13,200 --> 00:07:13,800 Speaker 4: doesn't fall down? 140 00:07:13,880 --> 00:07:16,200 Speaker 1: Yeah, I know all those cartoons could be injured. I 141 00:07:16,200 --> 00:07:17,520 Speaker 1: mean think about their families. 142 00:07:18,000 --> 00:07:20,600 Speaker 4: Yeah. I usually build in a safety factor of like 143 00:07:20,640 --> 00:07:24,119 Speaker 4: two or three to every cartoon I draw, just in case. 144 00:07:24,400 --> 00:07:27,440 Speaker 4: But yeah, but professional physicists do think about things in 145 00:07:27,480 --> 00:07:30,680 Speaker 4: a very different way than the rest of us. And 146 00:07:30,720 --> 00:07:33,240 Speaker 4: so that's the question we'll be exploring today. So to 147 00:07:33,280 --> 00:07:40,360 Speaker 4: the end the podcast will be tagling I to think 148 00:07:40,800 --> 00:07:42,200 Speaker 4: like a physicist. 149 00:07:42,600 --> 00:07:44,080 Speaker 1: And I'm not sure if this should be like an 150 00:07:44,080 --> 00:07:45,760 Speaker 1: instruction manual or like. 151 00:07:45,720 --> 00:07:48,920 Speaker 4: A warning, Oh why what can happen? 152 00:07:49,080 --> 00:07:51,000 Speaker 1: You know, like watch out for these signs that you're 153 00:07:51,000 --> 00:07:54,120 Speaker 1: thinking like a physicist, or like, hey, would you like 154 00:07:54,120 --> 00:07:56,480 Speaker 1: to think like a physicist? Here's steps one, two, five. 155 00:07:56,880 --> 00:07:59,880 Speaker 4: Well, I guess if it was the former, which titled 156 00:08:00,400 --> 00:08:02,600 Speaker 4: how to Not Think Like a Physicist, How to Avoid 157 00:08:02,640 --> 00:08:03,640 Speaker 4: Thinking like a Physicist. 158 00:08:04,080 --> 00:08:05,800 Speaker 1: We're going to get into the positives, I'm sure, but 159 00:08:05,840 --> 00:08:09,119 Speaker 1: you know, there is this lore that sometimes physicists oversimplify things. 160 00:08:09,120 --> 00:08:11,360 Speaker 1: They're like, come into a new field, They're like, oh, 161 00:08:11,440 --> 00:08:13,840 Speaker 1: these can just approximate this with a sphere, maybe a 162 00:08:13,880 --> 00:08:16,760 Speaker 1: line on it or whatever. There's this urban legend that 163 00:08:16,960 --> 00:08:20,000 Speaker 1: physicists being too simplistic, or the cause of the two 164 00:08:20,040 --> 00:08:23,160 Speaker 1: thousand and eight financial collapse, for example. So you know, 165 00:08:23,200 --> 00:08:25,880 Speaker 1: there are potentially some dangers to applying physics thinking to 166 00:08:25,920 --> 00:08:26,679 Speaker 1: the broader world. 167 00:08:27,360 --> 00:08:29,640 Speaker 4: Daniel, I wonder if you're overestimating how much people think 168 00:08:29,680 --> 00:08:30,520 Speaker 4: about physicists. 169 00:08:30,800 --> 00:08:33,719 Speaker 1: Probably I definitely don't have a clear view of that. 170 00:08:33,920 --> 00:08:36,080 Speaker 4: I mean, I think for an urban legend to exist, 171 00:08:36,160 --> 00:08:38,920 Speaker 4: you sort of need urban people talking about it. 172 00:08:42,480 --> 00:08:45,600 Speaker 1: Maybe that's just an urban legend within physics, maybe nobody else. 173 00:08:46,280 --> 00:08:50,600 Speaker 4: Yeah, just have issues. 174 00:08:51,600 --> 00:08:52,520 Speaker 1: We definitely do. 175 00:08:52,880 --> 00:08:55,000 Speaker 4: But it's an interesting question to ask if you're thinking 176 00:08:55,040 --> 00:08:59,560 Speaker 4: about following a career in physics or wondering what is 177 00:08:59,600 --> 00:09:01,480 Speaker 4: the job up and tail and what kind of mindset 178 00:09:01,520 --> 00:09:04,480 Speaker 4: do you have to have in order to do it? 179 00:09:04,520 --> 00:09:06,839 Speaker 4: At a research university or to become one, or to 180 00:09:06,880 --> 00:09:07,600 Speaker 4: get a degree in. 181 00:09:07,559 --> 00:09:09,960 Speaker 1: It, or if you're just an armchair physicist, if you 182 00:09:10,080 --> 00:09:13,240 Speaker 1: like thinking about the nature of the universe and making progress, 183 00:09:13,679 --> 00:09:16,400 Speaker 1: and over the years, maybe while listening to this podcast, 184 00:09:16,440 --> 00:09:19,440 Speaker 1: you've been putting together your own personal mental model of 185 00:09:19,600 --> 00:09:23,040 Speaker 1: the universe, asking questions, trying to click it together, coming 186 00:09:23,080 --> 00:09:27,000 Speaker 1: to a holistic understanding of how things work. In that case, 187 00:09:27,040 --> 00:09:28,440 Speaker 1: you might have picked up a few of the tricks 188 00:09:28,480 --> 00:09:29,800 Speaker 1: of thinking like a physicist. 189 00:09:29,960 --> 00:09:31,760 Speaker 4: Well, as usually, we were wondering how many people out 190 00:09:31,760 --> 00:09:34,640 Speaker 4: there had thought about this question, had maybe wondered what 191 00:09:34,720 --> 00:09:37,160 Speaker 4: it's like to be a professional physicist and what kind 192 00:09:37,160 --> 00:09:39,400 Speaker 4: of mental skills you need to be one. 193 00:09:39,720 --> 00:09:42,720 Speaker 1: Thanks very much to everybody who answers these random questions. 194 00:09:42,920 --> 00:09:45,800 Speaker 1: Love hearing your thoughts. Please don't be shy. If you 195 00:09:45,880 --> 00:09:47,559 Speaker 1: want to join the group, just write to me to 196 00:09:47,800 --> 00:09:50,240 Speaker 1: Questions at Danielandjorge dot com. 197 00:09:50,360 --> 00:09:51,960 Speaker 4: So think about it for a second. What do you 198 00:09:51,960 --> 00:09:54,960 Speaker 4: think it takes to think like a physicist. Here's what 199 00:09:55,000 --> 00:09:55,680 Speaker 4: people have to say. 200 00:09:56,320 --> 00:10:01,600 Speaker 5: A physicist must think of at least are and apply 201 00:10:01,679 --> 00:10:05,840 Speaker 5: it to the infinitely large universe, and that's not easy 202 00:10:05,880 --> 00:10:09,480 Speaker 5: to do. Hence the podcast for the rest of us. 203 00:10:10,440 --> 00:10:12,880 Speaker 6: What if that's an expression. I haven't heard of it before, 204 00:10:12,920 --> 00:10:16,320 Speaker 6: so it's value. I think it probably would refer to 205 00:10:16,320 --> 00:10:21,600 Speaker 6: someone being very practical, someone following the scientific method, very dogmatic, accurate, 206 00:10:22,000 --> 00:10:25,480 Speaker 6: but then some theoretical physicists that are a bit wacky 207 00:10:25,640 --> 00:10:27,520 Speaker 6: in what they come up with, so possibly a little 208 00:10:27,520 --> 00:10:28,040 Speaker 6: bit of that too. 209 00:10:28,800 --> 00:10:31,760 Speaker 4: I think like a physicist is to be asking questions 210 00:10:31,800 --> 00:10:34,400 Speaker 4: and be relentless in your quest for an answer. 211 00:10:34,800 --> 00:10:38,199 Speaker 1: I'd say thinking like a physicist means being curious and 212 00:10:38,840 --> 00:10:44,080 Speaker 1: searching for answers through trial and error and experiments. 213 00:10:44,120 --> 00:10:48,360 Speaker 7: Means there's our podcast about physicists, and to do it 214 00:10:48,400 --> 00:10:49,839 Speaker 7: with your cartoonist friend. 215 00:10:50,600 --> 00:10:55,800 Speaker 8: Basically, to think like a physicist means if you discover something, 216 00:10:56,320 --> 00:11:01,319 Speaker 8: you get to really terrible name that doesn't make sense. 217 00:11:02,280 --> 00:11:05,640 Speaker 7: I think it means to contemplate matter and energy and 218 00:11:05,679 --> 00:11:07,280 Speaker 7: their interactions with one another. 219 00:11:07,920 --> 00:11:10,960 Speaker 9: Well, from the episodes that I have listened to so far, 220 00:11:11,080 --> 00:11:15,320 Speaker 9: I would say that to think like a physicist means 221 00:11:15,320 --> 00:11:19,640 Speaker 9: to be inquisitive, to try to make connections between different 222 00:11:19,880 --> 00:11:24,240 Speaker 9: aspects facets of life, and wondering why and. 223 00:11:26,160 --> 00:11:26,679 Speaker 1: Trying to. 224 00:11:28,520 --> 00:11:32,200 Speaker 9: Better understand and explain the phenomena we see throughout our 225 00:11:32,280 --> 00:11:32,880 Speaker 9: daily lives. 226 00:11:33,400 --> 00:11:35,839 Speaker 4: All right, I like some of these answers. I guess 227 00:11:36,120 --> 00:11:39,360 Speaker 4: we're done because one of them said, we just need 228 00:11:39,400 --> 00:11:42,120 Speaker 4: to start on a podcast about physics. 229 00:11:43,400 --> 00:11:46,920 Speaker 1: And then give everything you discover a terrible name. These 230 00:11:46,920 --> 00:11:47,920 Speaker 1: are some juicy answers. 231 00:11:48,880 --> 00:11:51,160 Speaker 4: I guess people have been listening to our podcast. 232 00:11:52,280 --> 00:11:54,840 Speaker 1: I love these answers because there's so meta. They tell 233 00:11:54,880 --> 00:11:57,360 Speaker 1: me basically what people have learned from listening to the 234 00:11:57,400 --> 00:11:59,839 Speaker 1: podcast for all these years. It's fantastic. 235 00:12:00,200 --> 00:12:03,280 Speaker 4: Well, hopefully people are thinking a little bit more like scientists, 236 00:12:03,280 --> 00:12:06,680 Speaker 4: like rational thinkers because of this podcast, and also maybe 237 00:12:06,800 --> 00:12:08,880 Speaker 4: learning a little bit more about the universe and how 238 00:12:08,920 --> 00:12:11,920 Speaker 4: it all works down to the atomic level and the 239 00:12:11,960 --> 00:12:12,760 Speaker 4: galactic level. 240 00:12:13,040 --> 00:12:16,720 Speaker 1: Yeah, and not just absorbing facts and little bits of knowledge, 241 00:12:16,760 --> 00:12:20,880 Speaker 1: pieces of information, but also training yourself into how to 242 00:12:20,920 --> 00:12:24,360 Speaker 1: accumulate more information, how to fit those pieces of information together, 243 00:12:24,640 --> 00:12:27,080 Speaker 1: how to think about them. Science is more than just 244 00:12:27,080 --> 00:12:29,439 Speaker 1: what we've learned. It's how we're going to learn more. 245 00:12:30,440 --> 00:12:32,679 Speaker 4: How are we driving the distinction here between physicis and 246 00:12:32,720 --> 00:12:35,199 Speaker 4: just a regular scientist or do you mean how to 247 00:12:35,280 --> 00:12:36,160 Speaker 4: think like a scientist? 248 00:12:36,440 --> 00:12:38,560 Speaker 1: Yeah, it's a great question. I don't know the answer 249 00:12:38,559 --> 00:12:40,599 Speaker 1: to that. I'm probably not even the right person to 250 00:12:40,640 --> 00:12:43,200 Speaker 1: answer the question of how do physicists think? Because I'm 251 00:12:43,240 --> 00:12:46,880 Speaker 1: stuck in that mindset. I can't really see outside of 252 00:12:46,880 --> 00:12:49,440 Speaker 1: it to understand how other people think. But when I 253 00:12:49,480 --> 00:12:52,719 Speaker 1: mean chemists or biologists or economists, I do notice that 254 00:12:52,880 --> 00:12:55,880 Speaker 1: answer and ask questions in a different way. There's something 255 00:12:55,880 --> 00:12:58,880 Speaker 1: I have more in common with other physicists than I 256 00:12:59,000 --> 00:13:01,800 Speaker 1: have with other side scientists. So there's something to it. 257 00:13:02,720 --> 00:13:04,640 Speaker 4: All right, Well, let's dig into it. What do you 258 00:13:04,640 --> 00:13:07,240 Speaker 4: think is specific about how physicists think. 259 00:13:07,720 --> 00:13:10,280 Speaker 1: I think some of it comes from the fundamental motivation 260 00:13:10,480 --> 00:13:13,760 Speaker 1: and the assumptions that underlie physics. Like the goal is big. 261 00:13:14,040 --> 00:13:16,520 Speaker 1: We want to understand the universe. We want to figure 262 00:13:16,559 --> 00:13:20,000 Speaker 1: it out. And the assumptions are pretty basic. They're like, look, 263 00:13:20,040 --> 00:13:23,479 Speaker 1: the universe is understand a bull, and we can describe 264 00:13:23,480 --> 00:13:27,000 Speaker 1: it with mathematical laws. We can build a mental model. 265 00:13:27,360 --> 00:13:29,480 Speaker 1: The model should follow those laws, and we can use 266 00:13:29,520 --> 00:13:31,719 Speaker 1: it to like predict the future and to understand the 267 00:13:31,800 --> 00:13:34,680 Speaker 1: nature of the universe. You know, inherent in that is 268 00:13:34,720 --> 00:13:37,800 Speaker 1: that we are simplifying the universe. We're taking all these 269 00:13:37,840 --> 00:13:40,640 Speaker 1: observations and the weaving them together into a story. That's 270 00:13:40,640 --> 00:13:43,679 Speaker 1: what the mathematical model is. We're saying, here's how this works, 271 00:13:43,679 --> 00:13:46,679 Speaker 1: here's what's really happening behind the scene. So there's sort 272 00:13:46,679 --> 00:13:48,520 Speaker 1: of like an ambition there to say, like we can 273 00:13:48,559 --> 00:13:52,280 Speaker 1: describe the basic elements of the universe, whereas, and again 274 00:13:52,280 --> 00:13:54,600 Speaker 1: I'm not an expert in other fields, you know, they 275 00:13:54,640 --> 00:13:56,800 Speaker 1: feel a little bit more zoomed out, so they're not 276 00:13:56,800 --> 00:14:00,920 Speaker 1: always as ambitious about like the fundamental understanding. They're describing 277 00:14:00,960 --> 00:14:03,319 Speaker 1: things as sort of a higher level, which again still 278 00:14:03,360 --> 00:14:06,720 Speaker 1: requires mathematical modeling and great precision. It's not a question 279 00:14:06,760 --> 00:14:09,600 Speaker 1: of like precision or rigor just a question of like 280 00:14:09,679 --> 00:14:12,720 Speaker 1: the ambition the context of the questions you're asking. 281 00:14:13,000 --> 00:14:15,440 Speaker 4: Well, well, are you saying that other scientists are not 282 00:14:15,480 --> 00:14:16,199 Speaker 4: as ambitious? 283 00:14:17,720 --> 00:14:21,560 Speaker 1: I think maybe philosophically, physics and at least fundamental physics 284 00:14:21,560 --> 00:14:27,520 Speaker 1: and particle physics is asking more ambitious questions than other fields. Yeah, 285 00:14:27,800 --> 00:14:32,200 Speaker 1: I think they have deeper and broader implications again, philosophically. 286 00:14:31,720 --> 00:14:33,920 Speaker 4: Right, right, So you think your topic of research is 287 00:14:33,920 --> 00:14:38,560 Speaker 4: more important than other scientists because you're a physicist. I'm 288 00:14:38,560 --> 00:14:41,000 Speaker 4: just saying there might be a little bit of bias here. 289 00:14:40,840 --> 00:14:43,400 Speaker 1: A Daniel, No, it's totally reasonable to dig into that. 290 00:14:43,520 --> 00:14:46,840 Speaker 1: I wouldn't say more important. You know, somebody who's developing 291 00:14:47,440 --> 00:14:51,080 Speaker 1: new techniques to develop green energy, for example, they're not 292 00:14:51,160 --> 00:14:54,080 Speaker 1: answering deep and fundamental questions about the nature of reality, 293 00:14:54,320 --> 00:14:57,120 Speaker 1: but they're improving people's lives and maybe saving the planet, 294 00:14:57,160 --> 00:15:00,960 Speaker 1: So that's arguably much more important. I think in terms 295 00:15:00,960 --> 00:15:04,960 Speaker 1: of the philosophical context of our lives, particle physics and 296 00:15:05,000 --> 00:15:08,760 Speaker 1: fundamental physics is answering those questions. Whether that's important or 297 00:15:08,800 --> 00:15:11,520 Speaker 1: not is totally subjective, you know, whether it has value. 298 00:15:11,640 --> 00:15:14,120 Speaker 1: Every kind of science is answering different kinds of questions, 299 00:15:14,560 --> 00:15:17,400 Speaker 1: giving different kinds of insight into how the universe works. 300 00:15:18,320 --> 00:15:20,240 Speaker 1: For me, at least one of the appeals of fundamental 301 00:15:20,280 --> 00:15:22,800 Speaker 1: physics are these philosophical implications of it. 302 00:15:23,160 --> 00:15:25,680 Speaker 4: Right, Well, I think, you know, most scientists would agree 303 00:15:25,680 --> 00:15:27,920 Speaker 4: that what they're doing is also trying to understand and 304 00:15:27,960 --> 00:15:30,160 Speaker 4: explain the world. I wonder if maybe a lot of 305 00:15:30,160 --> 00:15:33,000 Speaker 4: the difference is just in the topic and the kinds 306 00:15:33,000 --> 00:15:36,160 Speaker 4: of things that you're looking at the scope of it, 307 00:15:36,640 --> 00:15:39,520 Speaker 4: or the kinds of phenomena you're looking at. 308 00:15:39,680 --> 00:15:41,800 Speaker 1: Yeah, I think that everybody is doing the thing they 309 00:15:41,840 --> 00:15:45,560 Speaker 1: think is most interesting and most exciting, And that's very personal, right. 310 00:15:45,760 --> 00:15:49,080 Speaker 1: The person who's like crouching in a rainforest watching spiders 311 00:15:49,120 --> 00:15:52,400 Speaker 1: crawl up twigs for hours and hours a day is 312 00:15:52,440 --> 00:15:55,160 Speaker 1: deeply fascinated by that and chose to do that instead 313 00:15:55,200 --> 00:15:58,360 Speaker 1: of economics or psychiatry or whatever for a reason, And 314 00:15:58,400 --> 00:16:01,280 Speaker 1: that's totally cool. You're right, and the choice of topic 315 00:16:01,480 --> 00:16:03,560 Speaker 1: is very very personal. But I think the choice of 316 00:16:03,560 --> 00:16:06,360 Speaker 1: topic also sometimes leads to a different way of thinking. 317 00:16:06,880 --> 00:16:10,000 Speaker 1: Like I think, because we're trying to ask fundamental questions 318 00:16:10,040 --> 00:16:12,600 Speaker 1: and deep questions about the universe, we feel like we 319 00:16:12,600 --> 00:16:15,840 Speaker 1: can touch onto some sort of mathematical purity, that there 320 00:16:15,960 --> 00:16:19,760 Speaker 1: is maybe mathematics that describes this that we can drill 321 00:16:19,840 --> 00:16:23,640 Speaker 1: down into and reveal. You know, somebody who's studying like hurricanes. 322 00:16:24,080 --> 00:16:26,480 Speaker 1: You know, we don't have any mathematics that describes hurricanes. 323 00:16:26,480 --> 00:16:28,920 Speaker 1: So we can do some simulations, but we're sort of 324 00:16:28,920 --> 00:16:31,280 Speaker 1: at a loss because of all the chaos and the details. 325 00:16:31,280 --> 00:16:34,440 Speaker 1: But when you zoom down into the fundamental firmament of 326 00:16:34,520 --> 00:16:37,280 Speaker 1: the universe, we hope maybe there is some mathematics there 327 00:16:37,320 --> 00:16:39,960 Speaker 1: that can describe what's going on. And so that's I 328 00:16:40,000 --> 00:16:43,360 Speaker 1: think why physicists tend to build these mental mathematical models 329 00:16:43,440 --> 00:16:46,800 Speaker 1: sometimes too simplified, you know, hence the famous spherical cowjoke, 330 00:16:46,840 --> 00:16:48,720 Speaker 1: which I don't know, maybe that's only a famous joke 331 00:16:48,720 --> 00:16:49,840 Speaker 1: within physics, you tell me. 332 00:16:50,480 --> 00:16:54,400 Speaker 4: I've never heard of that before. But uh, but you know, 333 00:16:54,440 --> 00:16:56,480 Speaker 4: I think all scientists would say that what they're doing 334 00:16:56,520 --> 00:16:58,760 Speaker 4: is fundamental as well. Like, if you're studying spiders, you're 335 00:16:58,760 --> 00:17:01,960 Speaker 4: probably thinking about the different ways that life can form, 336 00:17:02,160 --> 00:17:05,240 Speaker 4: or the different factors that go into creating life and 337 00:17:05,280 --> 00:17:07,920 Speaker 4: the factors that shape live. That seems pretty fundamental as well. 338 00:17:08,200 --> 00:17:11,320 Speaker 1: Yeah, and ambitious, And if anything, I think you probably 339 00:17:11,320 --> 00:17:13,840 Speaker 1: have a lot more insight into this than I do, 340 00:17:14,000 --> 00:17:16,600 Speaker 1: or than most people, because you interact with so many 341 00:17:16,600 --> 00:17:20,399 Speaker 1: different kinds of scientists, and obviously you've been spending a 342 00:17:20,400 --> 00:17:23,720 Speaker 1: lot of time learning about physics and decoding the brains 343 00:17:23,760 --> 00:17:27,200 Speaker 1: of physicists but also other scientists, and so from your perspective, 344 00:17:27,200 --> 00:17:29,360 Speaker 1: I'd be very curious to hear, like, do you think 345 00:17:29,400 --> 00:17:32,280 Speaker 1: physicists think differently? Is the mind of physicists trained at 346 00:17:32,280 --> 00:17:35,000 Speaker 1: different skills? Do they take a different approach or all 347 00:17:35,040 --> 00:17:36,560 Speaker 1: scientists just one category? 348 00:17:36,600 --> 00:17:40,480 Speaker 4: For you? You know, I think that if you're a scientist, 349 00:17:40,520 --> 00:17:42,399 Speaker 4: you're probably trying to figure out how the world and 350 00:17:42,480 --> 00:17:46,840 Speaker 4: the universe works. You're just asking questions about different phenomena 351 00:17:46,920 --> 00:17:50,200 Speaker 4: in it. You know, if you're someone who studies hurricanes, 352 00:17:50,200 --> 00:17:56,600 Speaker 4: you're trying to understand how certain physical processes work and 353 00:17:56,640 --> 00:18:00,000 Speaker 4: how they can come together to create large effects. For example, 354 00:18:00,040 --> 00:18:03,280 Speaker 4: that seems pretty fundamental as well or as fundamental as 355 00:18:03,320 --> 00:18:05,640 Speaker 4: asking you know what an atom is made of? 356 00:18:05,960 --> 00:18:09,159 Speaker 1: Yeah, can spiders come together to make hurricanes? Wouldn't that 357 00:18:09,240 --> 00:18:11,320 Speaker 1: be awesome? And shouldn't we pitch that show to the. 358 00:18:11,280 --> 00:18:13,400 Speaker 4: Discovery Channel Spider Nado's. 359 00:18:14,720 --> 00:18:15,560 Speaker 1: Spider Cane. 360 00:18:17,359 --> 00:18:21,200 Speaker 4: So I don't know, sorry, Signce Spider Natos sounds like 361 00:18:21,240 --> 00:18:21,639 Speaker 4: a winner. 362 00:18:22,119 --> 00:18:24,919 Speaker 1: Yeah, Well, I can't tell you whether it's fundamentally different 363 00:18:24,920 --> 00:18:27,720 Speaker 1: from the way other scientists think, because I'm not other scientists. 364 00:18:27,840 --> 00:18:29,959 Speaker 1: Maybe you can comment, but I can try to keep 365 00:18:29,960 --> 00:18:31,360 Speaker 1: you a little bit of an insight into the way 366 00:18:31,400 --> 00:18:34,119 Speaker 1: I approach a problem or the way I think about problems. 367 00:18:34,520 --> 00:18:37,879 Speaker 1: And that's this reliance on building a model. You know, 368 00:18:38,000 --> 00:18:39,959 Speaker 1: I look at a science problem like where is that 369 00:18:40,000 --> 00:18:42,640 Speaker 1: ball gonna land after it comes off the bat? Try 370 00:18:42,680 --> 00:18:44,919 Speaker 1: to predict that? And I think, well to get that 371 00:18:45,000 --> 00:18:47,680 Speaker 1: exactly right is way too complicated, and there's so many 372 00:18:47,680 --> 00:18:50,600 Speaker 1: factors involved. There's the wind speed, there's that bird flying by, 373 00:18:50,680 --> 00:18:53,199 Speaker 1: there's tufts in the air, et cetera. And so I 374 00:18:53,240 --> 00:18:55,520 Speaker 1: build a simpler model of the universe. I say, toss 375 00:18:55,520 --> 00:18:58,000 Speaker 1: out the real universe. Can we come up with a 376 00:18:58,040 --> 00:19:00,919 Speaker 1: simpler version of the universe and ask the question in 377 00:19:00,960 --> 00:19:03,120 Speaker 1: that universe, but build a model in such a way 378 00:19:03,119 --> 00:19:05,440 Speaker 1: that the answer in the simple universe is still relevant 379 00:19:05,520 --> 00:19:08,879 Speaker 1: to reality. So can we extract the crucial details of 380 00:19:08,920 --> 00:19:11,800 Speaker 1: the problem, put those into our model, and then use 381 00:19:11,840 --> 00:19:14,000 Speaker 1: that to answer the question. So you know, you don't care, 382 00:19:14,000 --> 00:19:15,920 Speaker 1: for example, about the color of the ball, you don't 383 00:19:15,920 --> 00:19:18,360 Speaker 1: care whether some kid in the stand is eating ice cream. 384 00:19:18,560 --> 00:19:21,560 Speaker 1: None of these details about glorious reality matter to answering 385 00:19:21,560 --> 00:19:24,679 Speaker 1: this question. So you build a simpler model specific to 386 00:19:24,720 --> 00:19:26,920 Speaker 1: that question because it's good at answering that question, not 387 00:19:27,040 --> 00:19:30,000 Speaker 1: every other question, And you use that to answer the question. 388 00:19:30,720 --> 00:19:32,919 Speaker 1: And you know you can argue philosophically like is that 389 00:19:32,960 --> 00:19:35,000 Speaker 1: model real, what does it mean about the universe if 390 00:19:35,040 --> 00:19:37,040 Speaker 1: it works, et cetera, et cetera. But that's sort of 391 00:19:37,119 --> 00:19:39,639 Speaker 1: to me the core of thinking like a physicist is 392 00:19:39,680 --> 00:19:42,040 Speaker 1: building a little mental model and then using that to 393 00:19:42,040 --> 00:19:42,960 Speaker 1: answer your questions. 394 00:19:43,040 --> 00:19:46,200 Speaker 4: Yeah, I think you're basically describing what any scientist does. 395 00:19:46,400 --> 00:19:49,480 Speaker 4: You know, chemists, biologists, they all work off models. I 396 00:19:49,480 --> 00:19:52,240 Speaker 4: mean probably the word model is the most used word 397 00:19:52,800 --> 00:19:56,600 Speaker 4: in all of science. Yeah, you know, biologists make models 398 00:19:56,640 --> 00:20:02,000 Speaker 4: about evolution, about gene interactions, about how molecules interact, or 399 00:20:02,040 --> 00:20:04,879 Speaker 4: how a species propagate, and things like that. But I 400 00:20:04,880 --> 00:20:06,639 Speaker 4: wonder if the difference with you is that you're making 401 00:20:06,680 --> 00:20:11,200 Speaker 4: models about the physical world or about baseballs, for example, 402 00:20:11,400 --> 00:20:12,679 Speaker 4: and not spiders. 403 00:20:14,400 --> 00:20:16,720 Speaker 1: Spiders are just way too complicated. There's no way for 404 00:20:16,760 --> 00:20:18,840 Speaker 1: me to build a model of a spider. I have 405 00:20:18,840 --> 00:20:23,240 Speaker 1: no idea exactly, and I know how to make the 406 00:20:23,280 --> 00:20:25,959 Speaker 1: approximation so that I can describe a baseball. I know 407 00:20:26,000 --> 00:20:29,560 Speaker 1: what to ignore. Maybe that's just my physics intuition, but 408 00:20:29,600 --> 00:20:31,120 Speaker 1: I don't know how to do that for a spider. 409 00:20:31,160 --> 00:20:33,280 Speaker 1: And I want to push back a little bit. I 410 00:20:33,320 --> 00:20:36,400 Speaker 1: do think there's a difference between the models built by 411 00:20:36,480 --> 00:20:40,080 Speaker 1: physicists and those built biologists, for example. I mean, in biology, 412 00:20:40,200 --> 00:20:44,040 Speaker 1: we know that every model we build is effective. It's 413 00:20:44,040 --> 00:20:49,399 Speaker 1: not fundamental, it's describing some emergent phenomenon like butterflies or spiders. 414 00:20:49,520 --> 00:20:53,040 Speaker 1: Something we know is not an inherent object in the universe, 415 00:20:53,080 --> 00:20:56,160 Speaker 1: but made out of those bits it comes together through 416 00:20:56,160 --> 00:21:00,240 Speaker 1: a special arrangement. So biology isn't describing something inherent to 417 00:21:00,280 --> 00:21:04,080 Speaker 1: the universe. It's just approximately describing how things work during 418 00:21:04,119 --> 00:21:07,840 Speaker 1: special conditions where like spiders and butterflies happen to emerge 419 00:21:07,840 --> 00:21:10,199 Speaker 1: because they don't always right. There's a long time in 420 00:21:10,200 --> 00:21:13,520 Speaker 1: the universe without spiders and butterflies, and so those rules 421 00:21:13,560 --> 00:21:16,879 Speaker 1: don't apply in those scenarios. But physics is trying to 422 00:21:16,920 --> 00:21:20,879 Speaker 1: figure out the fundamental laws, those that always apply in 423 00:21:21,000 --> 00:21:25,679 Speaker 1: all circumstances that are inherent to the universe. And that 424 00:21:25,840 --> 00:21:28,960 Speaker 1: difference in goal, I think leads to a different way 425 00:21:28,960 --> 00:21:31,800 Speaker 1: of thinking, you know, good or bad. It leads to 426 00:21:31,840 --> 00:21:35,320 Speaker 1: a hubris that we can describe anything with simple laws, 427 00:21:35,320 --> 00:21:38,720 Speaker 1: and it leads to different approaches and in different scientific culture, 428 00:21:38,800 --> 00:21:42,119 Speaker 1: so that physicists are kind of recognizable to others and 429 00:21:42,200 --> 00:21:43,480 Speaker 1: also to each other. 430 00:21:44,280 --> 00:21:47,120 Speaker 4: Well, you're married to a biologist, how does your way 431 00:21:47,119 --> 00:21:49,640 Speaker 4: of thinking different from your spouses? 432 00:21:49,800 --> 00:21:52,280 Speaker 1: Yeah, I think something that's different in between the way 433 00:21:52,320 --> 00:21:54,480 Speaker 1: that I think about things in the way biologists like 434 00:21:54,480 --> 00:21:56,800 Speaker 1: my wife think about things is we're definitely much more 435 00:21:56,800 --> 00:22:00,960 Speaker 1: focused on questions of like uncertainty and making things quantitative 436 00:22:01,800 --> 00:22:04,560 Speaker 1: in order to try to extract some knowledge. Sometimes the 437 00:22:04,640 --> 00:22:08,119 Speaker 1: things we're dealing with are abstract or indirect. You know, 438 00:22:08,119 --> 00:22:10,600 Speaker 1: we're talking about tiny particles or things we can't ever 439 00:22:10,680 --> 00:22:14,320 Speaker 1: see or even struggle to visualize. And so to help 440 00:22:14,400 --> 00:22:17,840 Speaker 1: us guide our thinking, we rely really heavily on the uncertainty. 441 00:22:17,960 --> 00:22:19,760 Speaker 1: How well do we know this? What can we say 442 00:22:19,800 --> 00:22:22,359 Speaker 1: about this? Because we don't have much intuition, We can't 443 00:22:22,359 --> 00:22:24,680 Speaker 1: like always got check our answers and say, is that 444 00:22:24,760 --> 00:22:27,440 Speaker 1: reasonable that the top quark lives for ten to the 445 00:22:27,520 --> 00:22:30,680 Speaker 1: minus twenty three seconds? I mean, you can't see that anyway. 446 00:22:31,400 --> 00:22:33,399 Speaker 1: Whereas you know, my wife, she can look at stuff 447 00:22:33,400 --> 00:22:35,400 Speaker 1: and oh is it growing? And did we get this right? 448 00:22:35,920 --> 00:22:39,000 Speaker 1: Is this virus killing that bacteria? Is somebody's got health 449 00:22:39,000 --> 00:22:41,600 Speaker 1: improving when they eat more chia seeds, this kind of stuff. 450 00:22:42,040 --> 00:22:44,240 Speaker 4: But she works with models as well, right, Her. 451 00:22:44,080 --> 00:22:46,520 Speaker 1: Grad students are really good looking. Yes, they're like models. 452 00:22:48,000 --> 00:22:51,320 Speaker 4: Yeah, yeah, well in comparison to physicists. 453 00:22:50,840 --> 00:22:54,680 Speaker 1: And ooh, you're right though that models is a very 454 00:22:54,720 --> 00:22:57,760 Speaker 1: abused word. Like I also work in the machine learning community, 455 00:22:57,760 --> 00:23:00,280 Speaker 1: and their model means to make very, very different than 456 00:23:00,320 --> 00:23:03,159 Speaker 1: a model in physics, than a model in fashion, and 457 00:23:03,200 --> 00:23:04,919 Speaker 1: so it's a very generic word, unfortunately. 458 00:23:06,400 --> 00:23:08,520 Speaker 4: But you think that maybe it's something to do with 459 00:23:08,600 --> 00:23:10,520 Speaker 4: the way that you look at the world and you 460 00:23:10,600 --> 00:23:12,520 Speaker 4: formulate models. But I guess I'm trying to say that 461 00:23:12,560 --> 00:23:15,280 Speaker 4: I think that's what all scientists do, right across different fields. 462 00:23:15,560 --> 00:23:17,680 Speaker 1: Yeah, so maybe physicists have more in common with other 463 00:23:17,720 --> 00:23:19,800 Speaker 1: scientists than I ever imagined. Happy to. 464 00:23:21,520 --> 00:23:23,720 Speaker 4: Sounds like you need to talk to people outside your 465 00:23:23,720 --> 00:23:28,359 Speaker 4: department a little more, baby, besides your spouse. How horten 466 00:23:28,400 --> 00:23:30,800 Speaker 4: to you interact with economists or chemists. 467 00:23:30,800 --> 00:23:33,439 Speaker 1: Economists very rarely, only if I run into them at 468 00:23:33,440 --> 00:23:38,200 Speaker 1: the park. Chemists and computer scientists and engineers much more common. 469 00:23:38,240 --> 00:23:41,240 Speaker 1: We sometimes have problems in common, you know, working on 470 00:23:41,400 --> 00:23:44,080 Speaker 1: electronics for a new technology you want to bury in 471 00:23:44,080 --> 00:23:46,800 Speaker 1: the ice in Antarctica, we need to understand the engineering 472 00:23:47,280 --> 00:23:50,760 Speaker 1: details of it, or thinking about how to apply machine 473 00:23:50,840 --> 00:23:54,160 Speaker 1: learning techniques we've developed for neutron stars to the problem 474 00:23:54,200 --> 00:23:58,120 Speaker 1: of like predicting organic synthesis, these kind of things. So, yeah, 475 00:23:58,240 --> 00:24:01,040 Speaker 1: definitely interact with the more physical science and engineering people 476 00:24:01,320 --> 00:24:04,080 Speaker 1: more often than like psychiatrists, but I also talk to 477 00:24:04,119 --> 00:24:06,400 Speaker 1: philosophers quite a bit. I don't know if they qualify 478 00:24:06,480 --> 00:24:08,080 Speaker 1: as scientists. 479 00:24:07,440 --> 00:24:10,600 Speaker 4: Do they? I think they're not. Usually they're not in 480 00:24:10,640 --> 00:24:12,639 Speaker 4: the same department for a reason, isn't it. 481 00:24:12,640 --> 00:24:15,880 Speaker 1: It's fascinating though, Actually people in the philosophy of physics 482 00:24:15,880 --> 00:24:19,240 Speaker 1: department here, they all have their PhDs in physics rather 483 00:24:19,280 --> 00:24:20,360 Speaker 1: than in philosophy. 484 00:24:20,480 --> 00:24:24,359 Speaker 4: Well so they're physicists who have a philosophy degree in 485 00:24:24,400 --> 00:24:26,960 Speaker 4: the philosophy of science physics. 486 00:24:28,080 --> 00:24:31,960 Speaker 1: A doctor of philosophy of physicists, but now they're professors 487 00:24:31,960 --> 00:24:33,480 Speaker 1: in philosophy of physics. 488 00:24:35,480 --> 00:24:38,639 Speaker 4: It sounds like what is it? The snake finally ate 489 00:24:38,680 --> 00:24:43,159 Speaker 4: its tail. It is interesting to think about how people 490 00:24:43,160 --> 00:24:45,840 Speaker 4: who are paid to do physics in particular think, and 491 00:24:46,200 --> 00:24:48,879 Speaker 4: what kinds of what makes them a tick I guess, 492 00:24:48,920 --> 00:24:51,600 Speaker 4: and how does that color how they see the world, 493 00:24:52,119 --> 00:24:54,480 Speaker 4: and so to get more insight into that, Danielle, you 494 00:24:54,520 --> 00:24:57,840 Speaker 4: interviewed a couple of physicists and one ex physicists. 495 00:24:57,920 --> 00:25:00,680 Speaker 1: Yeah, that's right. I talked to one physics who's made 496 00:25:00,720 --> 00:25:04,200 Speaker 1: it her mission to explain to people how physicists think 497 00:25:04,280 --> 00:25:09,280 Speaker 1: about uncertainty, and another whose job is to guide physicists 498 00:25:09,320 --> 00:25:12,840 Speaker 1: into the real world to find positions outside of academic 499 00:25:12,880 --> 00:25:13,880 Speaker 1: physics and research. 500 00:25:14,280 --> 00:25:16,560 Speaker 4: Well, it sounds like these are sort of like physics 501 00:25:16,600 --> 00:25:20,080 Speaker 4: translators or physics counselors. 502 00:25:22,560 --> 00:25:26,280 Speaker 1: Yeah, exactly, trying to bridge the gap between physicists and 503 00:25:26,440 --> 00:25:27,439 Speaker 1: actual human beings. 504 00:25:27,480 --> 00:25:28,879 Speaker 4: All right, Well, when we come back, we'll listen to 505 00:25:29,000 --> 00:25:33,800 Speaker 4: Daniel talking to two physicists whose jobs it is to 506 00:25:33,840 --> 00:25:37,280 Speaker 4: translate what physicists think and do to the rest of 507 00:25:37,359 --> 00:25:40,560 Speaker 4: the universe. 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US Dairy has 562 00:28:28,200 --> 00:28:32,639 Speaker 1: set themselves some ambitious sustainability goals, including being greenhouse gas 563 00:28:32,680 --> 00:28:35,200 Speaker 1: neutral by twenty to fifty That's why they're working hard 564 00:28:35,280 --> 00:28:37,719 Speaker 1: every day to find new ways to reduce waste, conserve 565 00:28:37,840 --> 00:28:41,600 Speaker 1: natural resources, and drive down greenhouse gas emissions. Take water, 566 00:28:41,640 --> 00:28:44,720 Speaker 1: for example, most dairy farms reuse water up to four 567 00:28:44,800 --> 00:28:48,240 Speaker 1: times the same water cools the milk, cleans equipment, washes 568 00:28:48,280 --> 00:28:51,080 Speaker 1: the barn, and irrigates the crops. How is US Dairy 569 00:28:51,120 --> 00:28:54,880 Speaker 1: tackling greenhouse gases. 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Visit 576 00:29:12,160 --> 00:29:15,000 Speaker 1: US dairy dot com slash sustainability to learn more. 577 00:29:23,240 --> 00:29:26,360 Speaker 4: BARB, we're asking the question how to think like a physicist, 578 00:29:26,920 --> 00:29:29,280 Speaker 4: and apparently that involves talking to more. 579 00:29:29,160 --> 00:29:32,760 Speaker 1: Physicists group Think like a Physicist. 580 00:29:36,160 --> 00:29:39,520 Speaker 4: All right, well, you got to interview to interesting people here, Daniels. 581 00:29:39,560 --> 00:29:43,240 Speaker 4: First one is doctor Jen Kyle. What does Jen Kyle do? 582 00:29:43,720 --> 00:29:47,160 Speaker 1: Jen Kyle is a theoretical physicist, but she also runs 583 00:29:47,240 --> 00:29:50,440 Speaker 1: the YouTube channel Think Like a Physicist, where she tries 584 00:29:50,480 --> 00:29:52,920 Speaker 1: to explain to you how to use the techniques and 585 00:29:53,040 --> 00:29:55,880 Speaker 1: tricks of physics to think about the world and also 586 00:29:56,040 --> 00:29:59,320 Speaker 1: to decode science results so you can get an understanding 587 00:29:59,320 --> 00:30:02,720 Speaker 1: for whether that newsflash you just read about black holes 588 00:30:02,960 --> 00:30:04,000 Speaker 1: is real or not? 589 00:30:04,400 --> 00:30:06,240 Speaker 4: And did she talk to non physicist to figure out 590 00:30:06,240 --> 00:30:08,920 Speaker 4: if a physicists thinking a unique. 591 00:30:08,680 --> 00:30:13,280 Speaker 1: Way through her YouTube channel? So yeah, via the comments section. 592 00:30:13,440 --> 00:30:17,400 Speaker 4: Oh boy, and we all know how productive those can be. 593 00:30:18,800 --> 00:30:21,400 Speaker 1: Great insights in the comment section as always. 594 00:30:21,560 --> 00:30:24,719 Speaker 4: All right, well, here's Daniel's interview with particle physicists and 595 00:30:24,840 --> 00:30:26,360 Speaker 4: YouTuber Jen Kyle. 596 00:30:30,600 --> 00:30:34,800 Speaker 1: So it's my pleasure to introduce the podcast doctor Jen Kyle. Jen, 597 00:30:34,840 --> 00:30:36,280 Speaker 1: thanks very much for joining us today. 598 00:30:36,400 --> 00:30:37,720 Speaker 7: Hi, great to be here. 599 00:30:38,760 --> 00:30:41,800 Speaker 1: Great tell us a little bit about yourself. What's your 600 00:30:41,800 --> 00:30:44,040 Speaker 1: background with your training, what are you up to now? 601 00:30:44,440 --> 00:30:51,440 Speaker 7: Ah? Well, I'm a theoretical particle physicist. I've done mostly 602 00:30:51,520 --> 00:30:55,080 Speaker 7: work on beyond the standard model physics. I've looked at 603 00:30:55,120 --> 00:31:00,960 Speaker 7: some things on dark matter and possible new new theories 604 00:31:00,960 --> 00:31:05,240 Speaker 7: of flavor in the cork and Lepton sectors, and I 605 00:31:05,360 --> 00:31:08,240 Speaker 7: basically dabbled in physics beyond what we know now. 606 00:31:08,640 --> 00:31:12,560 Speaker 1: Great, so you are definitely a trained and practicing physicist. 607 00:31:12,680 --> 00:31:14,840 Speaker 1: So tell me what does it mean to you to 608 00:31:15,120 --> 00:31:18,600 Speaker 1: think like a physicist? Can you remember learning how to 609 00:31:18,680 --> 00:31:20,800 Speaker 1: do that? Can you compare the way you think now 610 00:31:20,880 --> 00:31:23,400 Speaker 1: to the way you thought before you went to grad school? 611 00:31:23,480 --> 00:31:25,520 Speaker 1: What does it mean to think like a physicist? 612 00:31:26,880 --> 00:31:30,720 Speaker 7: I would definitely say it was not something that one 613 00:31:30,840 --> 00:31:34,120 Speaker 7: learns in one day. It's more of a practice that 614 00:31:35,520 --> 00:31:39,480 Speaker 7: you learn over many years. And I would say that 615 00:31:40,160 --> 00:31:46,240 Speaker 7: a large part of thinking like a physicist is knowing 616 00:31:46,280 --> 00:31:51,000 Speaker 7: how to draw conclusions from the universe and observations that 617 00:31:51,000 --> 00:31:54,560 Speaker 7: we make of it, but also always keeping in mind 618 00:31:55,120 --> 00:31:59,800 Speaker 7: how uncertain those conclusions that we draw from our observations 619 00:31:59,840 --> 00:32:00,680 Speaker 7: can possibly be. 620 00:32:01,800 --> 00:32:04,240 Speaker 1: What do you mean uncertain, Like we have a hunch 621 00:32:04,280 --> 00:32:06,400 Speaker 1: and we're not sure, Oh, we don't have enough information, 622 00:32:06,720 --> 00:32:09,040 Speaker 1: or we could be confused. What do you mean by. 623 00:32:09,040 --> 00:32:15,160 Speaker 7: Uncertain well, basically, we draw conclusions about the universe from 624 00:32:15,520 --> 00:32:20,000 Speaker 7: making observations and making measurements. So let's say that we 625 00:32:20,080 --> 00:32:23,480 Speaker 7: have some amazing new idea that someone has come up with, 626 00:32:23,600 --> 00:32:28,040 Speaker 7: but it hasn't been tested. It will make predictions about 627 00:32:28,280 --> 00:32:32,080 Speaker 7: the universe, and oftentimes these are predictions about the values 628 00:32:32,080 --> 00:32:35,080 Speaker 7: of certain quantities that we can measure, like the lifetime 629 00:32:35,080 --> 00:32:38,360 Speaker 7: of a particle or the rate of a certain process 630 00:32:38,400 --> 00:32:44,000 Speaker 7: that happens at the large Hadron collider. And we want 631 00:32:44,040 --> 00:32:47,440 Speaker 7: to test this new amazing hypothesis, so we go and 632 00:32:47,480 --> 00:32:51,920 Speaker 7: measure those quantities. And when we measure those quantities, we 633 00:32:52,480 --> 00:32:58,440 Speaker 7: use experimental apparatuses and techniques, but it's not possible to 634 00:32:58,520 --> 00:33:03,080 Speaker 7: ever have a perfect experiment. Whenever you get a measured 635 00:33:03,160 --> 00:33:05,080 Speaker 7: value of a quantity, it's always going to differ at 636 00:33:05,160 --> 00:33:07,280 Speaker 7: least a little bit from the true value of the 637 00:33:07,320 --> 00:33:09,160 Speaker 7: quantity that you're trying to measure. So, if you try 638 00:33:09,200 --> 00:33:12,720 Speaker 7: to measure the electron mass, you will get a measured 639 00:33:12,800 --> 00:33:14,440 Speaker 7: value of the electron mass, but it's not going to 640 00:33:14,480 --> 00:33:16,720 Speaker 7: be exactly the true value of the electron mass. 641 00:33:16,920 --> 00:33:18,920 Speaker 1: So let's make a little bit more concrete instead of 642 00:33:18,960 --> 00:33:22,000 Speaker 1: thinking about particle physics. Let's say somebody gives me a coin, 643 00:33:22,760 --> 00:33:25,280 Speaker 1: and I have a theory that this coin is not fair, 644 00:33:25,360 --> 00:33:28,680 Speaker 1: that it's going to favor heads right sixty six percent 645 00:33:28,800 --> 00:33:31,680 Speaker 1: or something, and then I can do an experiment to see, 646 00:33:31,680 --> 00:33:33,920 Speaker 1: well is it a fair coin by flipping it right 647 00:33:34,120 --> 00:33:37,000 Speaker 1: five hundred times. So I think you're saying that there's 648 00:33:37,080 --> 00:33:40,560 Speaker 1: uncertainty because even if I flip it a thousand times, 649 00:33:40,840 --> 00:33:44,680 Speaker 1: I'm never going to know precisely what the real probability 650 00:33:44,760 --> 00:33:46,760 Speaker 1: is because I'm not flipping an infinite number of times. 651 00:33:46,800 --> 00:33:48,080 Speaker 1: There's always some randomness. 652 00:33:48,360 --> 00:33:50,240 Speaker 7: Is that what you're saying exactly? 653 00:33:50,840 --> 00:33:55,200 Speaker 1: Okay, So there's uncertainty in our measurements because we don't 654 00:33:55,240 --> 00:33:58,120 Speaker 1: take infinitely long experiments and we don't have infinite amounts 655 00:33:58,160 --> 00:34:00,600 Speaker 1: of data. What are some other ways that we can 656 00:34:00,640 --> 00:34:03,400 Speaker 1: be wrong or uncertain about our conclusions? 657 00:34:04,840 --> 00:34:08,000 Speaker 7: Well, there are lots of ways that error can sneak 658 00:34:08,040 --> 00:34:14,759 Speaker 7: into measurements. For example, we make measurements using some kind 659 00:34:14,800 --> 00:34:19,239 Speaker 7: of experimental measurement apparatus. So, for example, let's say if 660 00:34:19,280 --> 00:34:23,200 Speaker 7: we're trying to measure any quantity, we're using some kind 661 00:34:23,200 --> 00:34:27,880 Speaker 7: of experimental apparatus to do it, and that apparatus is 662 00:34:27,920 --> 00:34:30,920 Speaker 7: going to have a finite resolution of some kind. So, 663 00:34:31,000 --> 00:34:34,640 Speaker 7: for example, let's say you're trying to measure the size 664 00:34:34,760 --> 00:34:37,760 Speaker 7: of an object in a room. You use a ruler, 665 00:34:38,200 --> 00:34:41,239 Speaker 7: and that ruler has a finite gradation on it. You 666 00:34:41,280 --> 00:34:45,520 Speaker 7: can't see down to the micron size using a ruler, 667 00:34:45,880 --> 00:34:49,160 Speaker 7: so there's automatically some level of uncertainty that's going to 668 00:34:49,200 --> 00:34:53,320 Speaker 7: come in because of effects like that. You may also 669 00:34:54,600 --> 00:34:58,759 Speaker 7: for very complicated measurements, like, for example, if you're trying 670 00:34:58,800 --> 00:35:01,440 Speaker 7: to measure a cross section the large Hadron collider, you 671 00:35:01,520 --> 00:35:08,920 Speaker 7: have very complicated measuring devices and you have to simulate 672 00:35:09,120 --> 00:35:11,480 Speaker 7: various parts of the not only the physics that you're 673 00:35:11,480 --> 00:35:16,200 Speaker 7: trying to understand, but the device, and those simulations will 674 00:35:16,239 --> 00:35:18,920 Speaker 7: never match up exactly well with reality. 675 00:35:19,320 --> 00:35:21,560 Speaker 1: So I think what you're saying is that sometimes to 676 00:35:21,640 --> 00:35:24,319 Speaker 1: do these experiments we have to use devices we don't 677 00:35:24,360 --> 00:35:27,759 Speaker 1: even really understand exactly how they work. Like if I'm 678 00:35:27,800 --> 00:35:30,920 Speaker 1: measuring an electron the lartadron collider, and I have some 679 00:35:31,120 --> 00:35:34,680 Speaker 1: device to measure an electrons energy, it's complicated to measure 680 00:35:34,680 --> 00:35:37,040 Speaker 1: an electrons energy, and I don't exactly know what happens 681 00:35:37,040 --> 00:35:39,520 Speaker 1: when an electron slams into a block of copper and 682 00:35:39,600 --> 00:35:43,200 Speaker 1: creates a huge shower of other particles. It's complicated physics, 683 00:35:43,239 --> 00:35:45,839 Speaker 1: and I could be wrong about what's going on in 684 00:35:45,880 --> 00:35:49,440 Speaker 1: my own experimental device that I built and designed, right. 685 00:35:49,520 --> 00:35:53,000 Speaker 7: Yes, In fact, we don't entirely understand our own measuring 686 00:35:53,040 --> 00:35:56,960 Speaker 7: devices perfectly, so we have to model them and simulate 687 00:35:57,000 --> 00:36:00,799 Speaker 7: them and sometimes compare those simulations to data in order 688 00:36:00,840 --> 00:36:04,399 Speaker 7: to current to improve those simulations and get a better 689 00:36:04,480 --> 00:36:06,600 Speaker 7: measurement of whatever it is we're trying to measure. 690 00:36:07,239 --> 00:36:09,880 Speaker 1: Right, So, like back to the coin example. You know, 691 00:36:09,960 --> 00:36:11,719 Speaker 1: it's easy to look at a coin and say, oh 692 00:36:11,760 --> 00:36:14,920 Speaker 1: it's heads or oh it's tails, But say it was harder, right, Say, 693 00:36:15,120 --> 00:36:17,160 Speaker 1: I couldn't just look at the coin. I needed to 694 00:36:17,200 --> 00:36:19,600 Speaker 1: have some little device that told me if it was 695 00:36:19,600 --> 00:36:21,680 Speaker 1: heads or tails, and that device I didn't really know 696 00:36:21,760 --> 00:36:24,480 Speaker 1: how it worked, and it wasn't always sure it was correct. 697 00:36:24,880 --> 00:36:27,480 Speaker 1: That would lead some like uncertainty into my measurement, right, 698 00:36:27,520 --> 00:36:30,480 Speaker 1: because it could be wrong, or I could think that 699 00:36:30,520 --> 00:36:33,120 Speaker 1: it's correct, but it's it's incorrect in some other ways. 700 00:36:33,520 --> 00:36:36,360 Speaker 7: Yes, And it might be using some pattern recognition software 701 00:36:36,440 --> 00:36:40,160 Speaker 7: that doesn't handle like certain light levels very well or 702 00:36:40,200 --> 00:36:42,400 Speaker 7: something like that. So yeah, it could make a mistake 703 00:36:42,440 --> 00:36:44,200 Speaker 7: every once in a while, until you you've got heads, 704 00:36:44,200 --> 00:36:45,880 Speaker 7: when you've got tails, or vice versa. 705 00:36:46,080 --> 00:36:49,120 Speaker 1: Yeah, and so in physics, we're very quantitative about this, right, 706 00:36:49,320 --> 00:36:51,799 Speaker 1: We're very specific when we measure something. We say, oh, 707 00:36:51,880 --> 00:36:54,680 Speaker 1: there's a two percent chance we've been wrong, or a 708 00:36:54,840 --> 00:36:56,800 Speaker 1: zero point zer or is or zero zero one percent 709 00:36:56,880 --> 00:36:59,600 Speaker 1: chance we're wrong. Why are we such sticklers about this 710 00:36:59,680 --> 00:37:03,400 Speaker 1: in physic Why are we such nerds about measuring precisely 711 00:37:03,719 --> 00:37:05,879 Speaker 1: how wrong we might be in physics? What do you think? 712 00:37:06,840 --> 00:37:10,720 Speaker 7: Well? I think that part of it is that physics 713 00:37:10,800 --> 00:37:12,919 Speaker 7: was one of the first fields to do a lot 714 00:37:12,920 --> 00:37:18,080 Speaker 7: of measurements. So if you're only doing ten measurements and 715 00:37:18,160 --> 00:37:22,400 Speaker 7: you think you'll screw up like one out of a thousand, 716 00:37:23,040 --> 00:37:26,439 Speaker 7: you're probably not too worried that you're that you're going 717 00:37:26,480 --> 00:37:30,440 Speaker 7: to produce a wrong result, or produce a result that 718 00:37:30,680 --> 00:37:33,919 Speaker 7: had a large statistical fluctuation where you didn't you didn't 719 00:37:33,920 --> 00:37:37,960 Speaker 7: screw up anything, and you're you're apparatus performed exactly correctly. 720 00:37:38,040 --> 00:37:41,400 Speaker 7: But nonetheless you've got very unlucky. If you think that 721 00:37:41,400 --> 00:37:44,880 Speaker 7: that probability is small and you're only making like ten measurements, 722 00:37:44,920 --> 00:37:47,080 Speaker 7: you're not too worried that you're going to publish a 723 00:37:47,160 --> 00:37:49,320 Speaker 7: result that's going to lead people down a wrong path. 724 00:37:50,200 --> 00:37:55,680 Speaker 7: But in particle physics, we make thousands of measurements, most 725 00:37:55,680 --> 00:37:57,680 Speaker 7: of which you never hear about in the news because 726 00:37:57,760 --> 00:38:01,239 Speaker 7: unfortunately most of them agree with the standard model. But 727 00:38:01,360 --> 00:38:03,719 Speaker 7: because we make so many, there's going to be some 728 00:38:04,080 --> 00:38:10,040 Speaker 7: just out of statistical fluctuations that happen to appear to 729 00:38:10,120 --> 00:38:14,319 Speaker 7: disagree a lot with what we expect, And so it's 730 00:38:14,440 --> 00:38:20,440 Speaker 7: very important to have a very strict criterion for deciding 731 00:38:20,480 --> 00:38:23,640 Speaker 7: when something disagrees with what we expect so much that 732 00:38:23,880 --> 00:38:24,920 Speaker 7: it must be interesting. 733 00:38:26,200 --> 00:38:28,040 Speaker 1: Yeah, I think that's probably true. Do you think it's 734 00:38:28,080 --> 00:38:31,719 Speaker 1: also because some of the things we're probing are sort 735 00:38:31,719 --> 00:38:35,120 Speaker 1: of invisible, so that our measurements are always going to 736 00:38:35,120 --> 00:38:37,880 Speaker 1: be indirect, you know, Like if somebody discovers a new 737 00:38:37,960 --> 00:38:41,040 Speaker 1: kind of turtle in biology, they're like, here's the turtle, 738 00:38:41,160 --> 00:38:42,880 Speaker 1: Like I can show you, look, this is a turtle, 739 00:38:42,920 --> 00:38:45,640 Speaker 1: Like nobody's confused about whether it's a turtle. But if 740 00:38:45,680 --> 00:38:47,840 Speaker 1: we're saying, hey, I discovered the squigglyon, it's not like 741 00:38:47,880 --> 00:38:49,960 Speaker 1: I can say I've got a pile of squigglyons here 742 00:38:50,000 --> 00:38:51,840 Speaker 1: they are let's all play with them. I have to 743 00:38:51,880 --> 00:38:54,040 Speaker 1: show you data, and the data has statistics, and we 744 00:38:54,080 --> 00:38:57,400 Speaker 1: have to make inference, and so it's always frustratingly indirect. 745 00:38:57,640 --> 00:38:59,719 Speaker 1: And I wonder if that's one reason why we have 746 00:38:59,760 --> 00:39:02,960 Speaker 1: to be such nerds about whether or not we've been confused, 747 00:39:03,040 --> 00:39:06,640 Speaker 1: because there's so many different steps between the physical reality 748 00:39:06,920 --> 00:39:08,399 Speaker 1: and the actual measurements we make. 749 00:39:08,880 --> 00:39:12,960 Speaker 7: Yeah, it's also the case that in particle physics, we're 750 00:39:13,000 --> 00:39:17,560 Speaker 7: also dealing with looking for processes in colliders that can 751 00:39:17,600 --> 00:39:20,360 Speaker 7: look a lot like other processes that we aren't actually 752 00:39:20,400 --> 00:39:23,719 Speaker 7: interested in. So it's not so much like we go 753 00:39:23,840 --> 00:39:25,720 Speaker 7: out into the world and we find a new turtle 754 00:39:25,719 --> 00:39:27,560 Speaker 7: and we bring it back and show people and say 755 00:39:27,560 --> 00:39:30,239 Speaker 7: this is a new turtle. It's more like, we go 756 00:39:30,360 --> 00:39:32,360 Speaker 7: out into the world and we find a new turtle 757 00:39:32,400 --> 00:39:36,040 Speaker 7: that looks very, very similar to a lot of other turtles, 758 00:39:36,400 --> 00:39:40,120 Speaker 7: and we bring that turtle and another thirty turtles back, 759 00:39:41,200 --> 00:39:44,719 Speaker 7: and we show the collection of turtles to our colleagues, 760 00:39:45,160 --> 00:39:48,320 Speaker 7: and we have to convince them that that one turtle 761 00:39:48,320 --> 00:39:51,200 Speaker 7: really is special, it's. 762 00:39:51,000 --> 00:39:53,520 Speaker 1: Not just the same turtle all the way down. Yeah, exactly. 763 00:39:53,960 --> 00:39:56,160 Speaker 1: And then we do experiments with those turtles, flipping them 764 00:39:56,239 --> 00:39:59,279 Speaker 1: to see if they're fair coins in that. So this 765 00:39:59,400 --> 00:40:01,480 Speaker 1: is the way that does this think about things. We're 766 00:40:01,719 --> 00:40:04,600 Speaker 1: really focused on what we've measured, how well we know it, 767 00:40:04,760 --> 00:40:09,359 Speaker 1: quantifying that uncertainty different ways we can be wrong when 768 00:40:09,400 --> 00:40:12,680 Speaker 1: we communicate our results to the public. This is a challenge, 769 00:40:12,760 --> 00:40:15,719 Speaker 1: right to express to them here's what we think, but 770 00:40:15,800 --> 00:40:18,880 Speaker 1: here's how much wrong we might be. What do you 771 00:40:18,920 --> 00:40:21,960 Speaker 1: think are the usual stumbling blocks for people who haven't 772 00:40:22,000 --> 00:40:24,920 Speaker 1: spent their lives learning to think like a physicist for 773 00:40:25,120 --> 00:40:28,759 Speaker 1: understanding uncertainties and what we mean by uncertainties when we 774 00:40:28,800 --> 00:40:29,439 Speaker 1: talk about them. 775 00:40:29,920 --> 00:40:34,200 Speaker 7: Well, I think one problem is that most of the 776 00:40:34,239 --> 00:40:37,600 Speaker 7: time in real life, when we're talking about needing to 777 00:40:37,680 --> 00:40:41,120 Speaker 7: know the value of some quantity. 778 00:40:40,400 --> 00:40:42,920 Speaker 1: We were not hold on, are you contrasting physics with 779 00:40:43,040 --> 00:40:46,320 Speaker 1: real life? Is that what you just did here? Are 780 00:40:46,360 --> 00:40:47,880 Speaker 1: you saying physics is not really for me? 781 00:40:47,920 --> 00:40:55,000 Speaker 7: They're the same thing. But in the ordinary life, where 782 00:40:55,040 --> 00:41:00,439 Speaker 7: we go outside and and you know, do things where 783 00:41:00,440 --> 00:41:05,400 Speaker 7: we're not looking at a computer screen, we do get 784 00:41:06,160 --> 00:41:09,879 Speaker 7: values for various quantities. Like if we're driving our car, 785 00:41:10,000 --> 00:41:13,480 Speaker 7: we do look at our speedometer hopefully and see what 786 00:41:13,640 --> 00:41:20,560 Speaker 7: speed we're getting. And generally the outside world isn't very 787 00:41:20,920 --> 00:41:24,040 Speaker 7: it's not used to giving us uncertainties on the numbers 788 00:41:24,040 --> 00:41:25,880 Speaker 7: that we get. So we look at that speedometer and 789 00:41:25,920 --> 00:41:28,319 Speaker 7: it tells us we're going fifty seven miles an hour, 790 00:41:28,400 --> 00:41:30,960 Speaker 7: but it doesn't put an error bar on it. And 791 00:41:31,000 --> 00:41:36,320 Speaker 7: also when we're learning things about either physics or anything 792 00:41:36,360 --> 00:41:41,280 Speaker 7: else in our education, at least in our earlier education, 793 00:41:41,520 --> 00:41:44,759 Speaker 7: usually the idea is, here are the principles that we 794 00:41:44,840 --> 00:41:47,600 Speaker 7: work from. What can we figure out from it? But 795 00:41:47,640 --> 00:41:50,279 Speaker 7: we don't actually stop and think, well, what are the 796 00:41:50,320 --> 00:41:53,319 Speaker 7: experimental results that led to us having those principles, and 797 00:41:53,360 --> 00:41:56,759 Speaker 7: what were the errors on those principles? What were the 798 00:41:56,840 --> 00:42:00,680 Speaker 7: uncertainties on those principles? And you know, how well does 799 00:42:00,800 --> 00:42:04,960 Speaker 7: that principle work with the situation I'm trying to trying 800 00:42:04,960 --> 00:42:07,480 Speaker 7: to study at the moment. Am I actually using the 801 00:42:08,960 --> 00:42:12,879 Speaker 7: right set of scientific principles for the situation at hand? 802 00:42:12,920 --> 00:42:18,799 Speaker 7: Or am I introducing some uncertainties that maybe maybe I 803 00:42:18,920 --> 00:42:22,040 Speaker 7: need to think about. So I would say that the 804 00:42:22,080 --> 00:42:26,400 Speaker 7: main stumbling block is that we just aren't exposed to it. 805 00:42:29,120 --> 00:42:30,319 Speaker 7: It's it's hard to come by. 806 00:42:30,960 --> 00:42:33,719 Speaker 1: Yeah, I see. So maybe when you get pulled over, 807 00:42:33,960 --> 00:42:36,359 Speaker 1: you can tell the officer like, look, it said it 808 00:42:36,400 --> 00:42:38,399 Speaker 1: was I was doing sixty. I don't know why your 809 00:42:38,520 --> 00:42:40,719 Speaker 1: machine says I was doing eighty five. Maybe there's some 810 00:42:40,760 --> 00:42:43,320 Speaker 1: mistakes somewhere, right, Sometimes we have a little bit of 811 00:42:43,360 --> 00:42:46,480 Speaker 1: intuitive grasp of like, maybe there's fuzz in the numbers. 812 00:42:47,040 --> 00:42:50,040 Speaker 1: But you're right, we're rarely like measuring the uncertainties in 813 00:42:50,400 --> 00:42:53,319 Speaker 1: quote unquote real life. So for people who are not 814 00:42:53,520 --> 00:42:56,160 Speaker 1: trained like a physicist and don't nerd out about statistics 815 00:42:56,160 --> 00:42:58,680 Speaker 1: all the time, it's a sort of intuitive or easy 816 00:42:58,719 --> 00:43:02,040 Speaker 1: way to start to think think about these uncertainties. What 817 00:43:02,080 --> 00:43:04,560 Speaker 1: do you recommend I know you have a wonderful YouTube 818 00:43:04,600 --> 00:43:06,600 Speaker 1: channel where you teach people to think like a physicist 819 00:43:06,680 --> 00:43:10,120 Speaker 1: and think about uncertainties. How should people get started thinking 820 00:43:10,160 --> 00:43:11,880 Speaker 1: about uncertainties like a physicist? 821 00:43:12,200 --> 00:43:14,200 Speaker 7: Well, if you want to think about it the way 822 00:43:14,320 --> 00:43:18,680 Speaker 7: physicists do, I guess I would explain how physicists arrive 823 00:43:18,719 --> 00:43:21,719 Speaker 7: at those uncertainties. So, like a physicist who's conducting some 824 00:43:21,800 --> 00:43:25,319 Speaker 7: kind of an experiment, they are going to want to 825 00:43:25,480 --> 00:43:27,400 Speaker 7: produce a result, and they're going to want to produce 826 00:43:27,760 --> 00:43:29,640 Speaker 7: an error bar that goes with that result, that tells 827 00:43:29,680 --> 00:43:31,799 Speaker 7: you and what the uncertainty on that result is. 828 00:43:32,080 --> 00:43:34,359 Speaker 1: Let's stop there firm and describe exactly what you mean. 829 00:43:34,400 --> 00:43:37,560 Speaker 1: They're like the error bar. So if I say I've 830 00:43:37,719 --> 00:43:40,640 Speaker 1: measured my speed to be seventy miles an hour with 831 00:43:40,680 --> 00:43:43,400 Speaker 1: an air bar of five, what does that mean? What 832 00:43:43,440 --> 00:43:45,239 Speaker 1: does the error bar mean? What am I saying when 833 00:43:45,239 --> 00:43:45,799 Speaker 1: I say five? 834 00:43:46,120 --> 00:43:50,520 Speaker 7: So the error bar, if you're at least thinking about 835 00:43:50,560 --> 00:43:53,799 Speaker 7: it from a physicist point of view, is you've thought 836 00:43:53,840 --> 00:43:58,160 Speaker 7: about what the possible sources of error that can come in, 837 00:43:58,440 --> 00:44:00,839 Speaker 7: the ways that you could be wrong, the ways that 838 00:44:00,880 --> 00:44:04,920 Speaker 7: you could measure it incorrectly, and you've done some kind 839 00:44:05,040 --> 00:44:08,560 Speaker 7: of analysis or thinking about it to add those sources 840 00:44:08,600 --> 00:44:13,640 Speaker 7: together and figure out, roughly typically how much you would 841 00:44:13,680 --> 00:44:14,520 Speaker 7: be wrong by. 842 00:44:15,200 --> 00:44:18,200 Speaker 1: So does that mean that if I measure my speed 843 00:44:18,200 --> 00:44:20,680 Speaker 1: to be seventy plus or minus five, that the true 844 00:44:20,680 --> 00:44:24,879 Speaker 1: speed is definitely within sixty five to seventy five? 845 00:44:25,000 --> 00:44:25,080 Speaker 11: Like? 846 00:44:25,120 --> 00:44:28,200 Speaker 1: Does the error bar completely define the possible extent of 847 00:44:28,239 --> 00:44:28,680 Speaker 1: the truth? 848 00:44:29,000 --> 00:44:34,640 Speaker 7: Absolutely not. It's a typical value. It's a typical value 849 00:44:34,640 --> 00:44:37,520 Speaker 7: for the difference between the true value of something and 850 00:44:37,600 --> 00:44:42,320 Speaker 7: the value that we measure. And we don't know whether 851 00:44:42,400 --> 00:44:45,120 Speaker 7: the value we measure is above the true value or 852 00:44:45,160 --> 00:44:48,080 Speaker 7: below it. And we don't know if the difference between 853 00:44:48,120 --> 00:44:51,000 Speaker 7: the true value and our measured value is larger than 854 00:44:51,040 --> 00:44:53,160 Speaker 7: that error bar or smaller than that error bar in 855 00:44:53,200 --> 00:44:56,719 Speaker 7: an instance of a specific measurement. What that error bar 856 00:44:56,840 --> 00:45:00,520 Speaker 7: means is that's a typical value for how the true 857 00:45:00,560 --> 00:45:03,279 Speaker 7: value in the measured value would would disagree. 858 00:45:04,000 --> 00:45:07,399 Speaker 1: Right, And so if we quote seventy plus or minus five, 859 00:45:07,560 --> 00:45:10,720 Speaker 1: or let's talk about you know, politics, Joe Biden's polling 860 00:45:10,800 --> 00:45:16,200 Speaker 1: numbers are forty four percent with a uncertainty of three percent. Right, 861 00:45:16,480 --> 00:45:21,200 Speaker 1: that doesn't mean that his true value is between you know, 862 00:45:21,239 --> 00:45:24,440 Speaker 1: forty four plus three and forty four minus three. It 863 00:45:24,600 --> 00:45:27,839 Speaker 1: means that there's a sixty percent chance that it is, 864 00:45:28,360 --> 00:45:30,640 Speaker 1: and then therefore there's a thirty two percent chance that 865 00:45:30,680 --> 00:45:34,200 Speaker 1: it isn't. Right. So the airbar tells us, as you say, 866 00:45:34,719 --> 00:45:38,480 Speaker 1: roughly the size of the expected difference between the truths 867 00:45:38,480 --> 00:45:40,839 Speaker 1: and the measured value. But it doesn't bound it, right, 868 00:45:40,880 --> 00:45:43,640 Speaker 1: It doesn't tell us it's exactly within that. I see 869 00:45:43,680 --> 00:45:47,120 Speaker 1: this sort of misunderstanding all the time in political journalism. 870 00:45:47,320 --> 00:45:49,879 Speaker 1: You know, where they have two candidates and if they're 871 00:45:49,920 --> 00:45:53,960 Speaker 1: separated by ten points and the uncertainty is four points. 872 00:45:54,200 --> 00:45:56,440 Speaker 1: Then they say, okay, it's definitely a lead, but you know, 873 00:45:56,520 --> 00:45:59,600 Speaker 1: it still could be the opposite, or two candidates who 874 00:45:59,640 --> 00:46:02,920 Speaker 1: are new or each other, but within the statistical uncertainty, 875 00:46:02,920 --> 00:46:05,000 Speaker 1: they call it a tie, even though if one of 876 00:46:05,040 --> 00:46:07,440 Speaker 1: them has a larger value, we're pretty sure that you know, 877 00:46:07,440 --> 00:46:10,560 Speaker 1: we're somewhat sure at least that they have more support. 878 00:46:11,000 --> 00:46:13,200 Speaker 1: I think there's a lot of misunderstanding about what this 879 00:46:13,400 --> 00:46:16,520 Speaker 1: error bar means. It seems so much more definitive right 880 00:46:16,600 --> 00:46:18,239 Speaker 1: than the way that we meet it. It's really, as 881 00:46:18,280 --> 00:46:21,360 Speaker 1: you say, just a typical value. It tells you roughly 882 00:46:21,400 --> 00:46:25,120 Speaker 1: the scale of how far off you might be. So 883 00:46:25,200 --> 00:46:28,120 Speaker 1: when people are out there reading a scientific result, right 884 00:46:28,160 --> 00:46:31,160 Speaker 1: when they're not measuring their speedometer, when they're reading a 885 00:46:31,160 --> 00:46:33,920 Speaker 1: paper about a new particle and they come across something, 886 00:46:34,440 --> 00:46:37,200 Speaker 1: what should they be asking themselves? What they should what 887 00:46:37,200 --> 00:46:39,640 Speaker 1: should they be looking for in that article? To help 888 00:46:39,719 --> 00:46:44,000 Speaker 1: understand how uncertain are physicists about this new, squeakly unparticle. 889 00:46:44,280 --> 00:46:47,920 Speaker 7: Well, I mean, at the most basic level, if the 890 00:46:47,960 --> 00:46:52,120 Speaker 7: result is measuring something and saying this value was larger 891 00:46:52,160 --> 00:46:54,520 Speaker 7: than what we were expecting from our prediction, If the 892 00:46:54,520 --> 00:46:58,480 Speaker 7: particle didn't exist. The first question is to ask, well, 893 00:46:58,760 --> 00:47:01,160 Speaker 7: what was the difference between what was observed and what 894 00:47:01,320 --> 00:47:04,320 Speaker 7: was expected if the particle didn't exist, And then how 895 00:47:04,360 --> 00:47:09,359 Speaker 7: does that difference compare to the quoted uncertainty. So if 896 00:47:09,400 --> 00:47:13,319 Speaker 7: that difference is a lot larger than the quoted uncertainty, 897 00:47:13,400 --> 00:47:16,040 Speaker 7: then we would tend to think that something interesting is 898 00:47:16,080 --> 00:47:19,160 Speaker 7: going on. You know, maybe it's particle discovery. Hopefully it's 899 00:47:19,160 --> 00:47:22,439 Speaker 7: particle discovery, but it always could be that something has 900 00:47:22,480 --> 00:47:25,520 Speaker 7: gone wrong with the experiment that we don't understand. On 901 00:47:25,560 --> 00:47:29,319 Speaker 7: the other hand, if the difference between what's observed and 902 00:47:29,400 --> 00:47:34,480 Speaker 7: what's expected from the no new particle hypothesis, if that 903 00:47:34,640 --> 00:47:37,600 Speaker 7: difference is not much larger than the uncertainty, or maybe 904 00:47:37,600 --> 00:47:41,480 Speaker 7: it's only a couple times the uncertainty, then it's probably 905 00:47:41,520 --> 00:47:44,160 Speaker 7: a little bit too early to get excited. We need 906 00:47:44,200 --> 00:47:46,719 Speaker 7: more data and we need more results and possibly more 907 00:47:46,760 --> 00:47:50,200 Speaker 7: experiments to look at it before we say anything definitive. 908 00:47:51,040 --> 00:47:53,080 Speaker 1: Right, So then let's make a concrete go back to 909 00:47:53,160 --> 00:47:55,800 Speaker 1: our coin that we're tossing or the turtle that we're flipping. 910 00:47:56,280 --> 00:47:59,319 Speaker 1: Let's say I flip the coin two times and I 911 00:47:59,400 --> 00:48:03,040 Speaker 1: get too heads, so it's one hundred percent heads, right, 912 00:48:03,760 --> 00:48:06,160 Speaker 1: And then I go off and I write a paper saying, look, 913 00:48:06,280 --> 00:48:08,960 Speaker 1: my coin is one hundred percent heads. It's totally unfair. 914 00:48:09,000 --> 00:48:11,839 Speaker 1: And you're the reviewer. You might look and say, all right, 915 00:48:11,880 --> 00:48:14,160 Speaker 1: you know, but the prediction for a fair coin is 916 00:48:14,160 --> 00:48:16,640 Speaker 1: fifty percent, and the prediction for an unfair coin is 917 00:48:16,960 --> 00:48:19,719 Speaker 1: you know, something above that. But the uncertainty on your 918 00:48:19,760 --> 00:48:23,239 Speaker 1: measurement is huge because you only flipped it twice, right, So, yes, 919 00:48:23,280 --> 00:48:25,080 Speaker 1: you measured one hundred percent heads, but you could have 920 00:48:25,120 --> 00:48:27,840 Speaker 1: also gotten fifty percent heads or seventy five percent heads 921 00:48:27,960 --> 00:48:30,719 Speaker 1: or whatever. And so you're saying if I go back 922 00:48:31,000 --> 00:48:33,720 Speaker 1: and then flip it a million times and I still 923 00:48:33,800 --> 00:48:36,880 Speaker 1: get a million heads, that that's very different, right, And 924 00:48:36,920 --> 00:48:39,440 Speaker 1: I think people can understand that that's much more compelling. 925 00:48:39,760 --> 00:48:41,359 Speaker 1: If you get a million heads in a row, it's 926 00:48:41,480 --> 00:48:45,000 Speaker 1: very unlikely to be a fair coin. And that's the difference, right, 927 00:48:45,000 --> 00:48:48,200 Speaker 1: that there's a smaller uncertainty on my measurement of one 928 00:48:48,239 --> 00:48:50,760 Speaker 1: hundred percent heads if I flip it a million times 929 00:48:50,760 --> 00:48:53,920 Speaker 1: and if I flip it two times. And so the 930 00:48:54,239 --> 00:48:57,240 Speaker 1: two different hypotheses of like a fair coin fifty percent 931 00:48:57,280 --> 00:48:59,320 Speaker 1: heads and an unfair coin and one hundred percent heads. 932 00:49:00,080 --> 00:49:02,960 Speaker 1: The difference there is now large compared to the uncertainty, 933 00:49:03,280 --> 00:49:05,520 Speaker 1: whereas it was small when I only flipped it twice. 934 00:49:05,840 --> 00:49:08,279 Speaker 7: Yeah, when you only flip it twice, I mean, even 935 00:49:08,280 --> 00:49:11,280 Speaker 7: if the coin is fair, the probability is twenty five percent, 936 00:49:11,280 --> 00:49:14,480 Speaker 7: it's going to come up heads both times. So it's 937 00:49:14,840 --> 00:49:16,799 Speaker 7: important to not jump the gun and think that you've 938 00:49:16,800 --> 00:49:22,000 Speaker 7: discovered something amazing when you might just have a quarter exactly. 939 00:49:22,080 --> 00:49:23,960 Speaker 7: On the other hand, if you flip the coin ten 940 00:49:24,040 --> 00:49:27,080 Speaker 7: times and it comes up heads each time, well then 941 00:49:27,239 --> 00:49:30,960 Speaker 7: you know, you start to think maybe something's up. And 942 00:49:31,040 --> 00:49:33,880 Speaker 7: if you do it twenty times, then you might start 943 00:49:33,920 --> 00:49:36,640 Speaker 7: to really think that's something up. And certainly, if you 944 00:49:36,760 --> 00:49:39,399 Speaker 7: flip it a million times then you're pretty darn certain 945 00:49:39,520 --> 00:49:40,280 Speaker 7: something's use. 946 00:49:41,760 --> 00:49:46,680 Speaker 1: Exactly. But I think it's fascinating that even now, for example, 947 00:49:47,040 --> 00:49:50,680 Speaker 1: we can't say one hundred percent definitively that the Higgs 948 00:49:50,719 --> 00:49:53,799 Speaker 1: boson exists, like we've taken so much data, we have 949 00:49:54,040 --> 00:49:57,799 Speaker 1: so much evidence, and yet still it could all be 950 00:49:57,800 --> 00:50:00,680 Speaker 1: a fluctuation, right, It could all just be We could 951 00:50:00,680 --> 00:50:03,120 Speaker 1: be that situation where we flip the a fair coin 952 00:50:03,200 --> 00:50:05,640 Speaker 1: a million times and gotten a million heads in a row. 953 00:50:05,719 --> 00:50:08,480 Speaker 1: It can happen, and we could have been fooled by 954 00:50:08,480 --> 00:50:10,240 Speaker 1: our data. We don't have like a pile of Higgs 955 00:50:10,280 --> 00:50:12,720 Speaker 1: bosons we can point to and say these are them, folks. 956 00:50:13,120 --> 00:50:15,720 Speaker 1: We just have, you know, basically the result of flipping 957 00:50:15,760 --> 00:50:18,759 Speaker 1: a bunch of coins and seeing it come out weird 958 00:50:19,320 --> 00:50:22,719 Speaker 1: compared to our prediction for no Higgs boson. So in principle, 959 00:50:23,040 --> 00:50:25,400 Speaker 1: we know we don't really know that any particle is 960 00:50:25,440 --> 00:50:28,200 Speaker 1: out there, though, I guess as we continue to make 961 00:50:28,239 --> 00:50:31,000 Speaker 1: collisions and analyze data, we get more and more certain. 962 00:50:31,200 --> 00:50:33,000 Speaker 1: But it's sort of like approaching the speed of light, right, 963 00:50:33,040 --> 00:50:34,560 Speaker 1: you can never actually get there. 964 00:50:34,719 --> 00:50:38,600 Speaker 7: That's right. You can never be absolutely certain of any 965 00:50:38,640 --> 00:50:41,640 Speaker 7: scientific result that you produce. But on the other hand, 966 00:50:41,640 --> 00:50:44,439 Speaker 7: you can also not be absolutely certain that this chair 967 00:50:44,520 --> 00:50:47,399 Speaker 7: sitting next to you actually exists, because of course your 968 00:50:47,560 --> 00:50:51,440 Speaker 7: your eyes could have malfunctioned, you could be dreaming. So 969 00:50:52,400 --> 00:50:57,000 Speaker 7: certainty is is a dream. It's an illusion. It's not 970 00:50:57,040 --> 00:50:58,000 Speaker 7: something we can ever achieve. 971 00:50:58,040 --> 00:51:04,759 Speaker 1: Exactly right, I'm not one hundred certain we're having this conversation, Yeah, exactly, great, Well, 972 00:51:04,920 --> 00:51:07,560 Speaker 1: so tell us more about your project. Think like a 973 00:51:07,600 --> 00:51:09,680 Speaker 1: physicist where people can go to learn more about it 974 00:51:09,760 --> 00:51:12,279 Speaker 1: and learn more about thinking like a physicist. 975 00:51:12,520 --> 00:51:15,560 Speaker 7: Yeah, so I have a YouTube channel. It's called think 976 00:51:15,640 --> 00:51:18,440 Speaker 7: like a Physicist, And the idea behind my channel is 977 00:51:18,480 --> 00:51:22,200 Speaker 7: I wanted to take the statistical methods, especially also the 978 00:51:22,280 --> 00:51:25,480 Speaker 7: other methods the physicists use, but especially the statistical methods 979 00:51:25,480 --> 00:51:28,680 Speaker 7: the physicists use, and I wanted to explain them in 980 00:51:28,719 --> 00:51:33,040 Speaker 7: a way that I hope non scientists can understand. And 981 00:51:33,520 --> 00:51:36,359 Speaker 7: the idea is that I would like for people when 982 00:51:36,400 --> 00:51:38,520 Speaker 7: they read about a scientific result and it has an 983 00:51:38,600 --> 00:51:40,440 Speaker 7: error bar on it, that they would be able to 984 00:51:40,480 --> 00:51:43,200 Speaker 7: have a better understanding of what that error bar means, 985 00:51:43,840 --> 00:51:48,280 Speaker 7: and also that that way they can understand scientific results 986 00:51:48,320 --> 00:51:54,440 Speaker 7: in context. For example, if you hear that one experiment 987 00:51:54,600 --> 00:51:57,520 Speaker 7: does a measurement of a certain quantity and it agrees 988 00:51:57,560 --> 00:52:01,200 Speaker 7: with the standard model, and then three years later you 989 00:52:01,239 --> 00:52:04,239 Speaker 7: hear that another experiment measured the same quantity and they 990 00:52:04,800 --> 00:52:07,520 Speaker 7: got a different result, you know it might be because 991 00:52:07,560 --> 00:52:11,080 Speaker 7: the second experiment had a smaller error bar than the 992 00:52:11,120 --> 00:52:14,560 Speaker 7: first one did, and so you can understand results in 993 00:52:14,640 --> 00:52:19,880 Speaker 7: context better. So basically, I go through a lot of 994 00:52:19,920 --> 00:52:26,400 Speaker 7: the basic statistical techniques that physicists use, and I hope 995 00:52:26,400 --> 00:52:28,680 Speaker 7: that I explained them in a way that people can understand, 996 00:52:29,440 --> 00:52:33,760 Speaker 7: and so yeah, I would very much like the public 997 00:52:33,760 --> 00:52:35,359 Speaker 7: to know more about these topics so that they can 998 00:52:35,440 --> 00:52:36,719 Speaker 7: understand what we do a bit better. 999 00:52:37,120 --> 00:52:39,120 Speaker 1: Great, tell us one more time where people can find you. 1000 00:52:39,600 --> 00:52:42,200 Speaker 7: Yeah, my YouTube channel is called Think like a Physicist. 1001 00:52:42,440 --> 00:52:44,840 Speaker 1: Great. Well, thanks very much Jen for coming on podcast 1002 00:52:44,920 --> 00:52:47,680 Speaker 1: today and thinking like a Physicist with me. I appreciate it. 1003 00:52:47,719 --> 00:52:49,080 Speaker 7: Thank you so much. It's been great. 1004 00:52:49,840 --> 00:52:52,520 Speaker 4: All right, interesting interview. I like how you talked about 1005 00:52:52,600 --> 00:52:56,000 Speaker 4: uncertainties and how you know this concept, you know, spills 1006 00:52:56,040 --> 00:52:59,160 Speaker 4: into our everyday lives, especially when it comes to things 1007 00:52:59,160 --> 00:53:02,000 Speaker 4: like policies, But people don't seem to have a pretty 1008 00:53:02,280 --> 00:53:05,080 Speaker 4: good understanding of that. Maybe they should talk to statisticians, 1009 00:53:06,040 --> 00:53:08,000 Speaker 4: not physicists or politicians. 1010 00:53:08,040 --> 00:53:10,040 Speaker 1: How to think like a statistician exactly? 1011 00:53:10,680 --> 00:53:13,600 Speaker 4: Yeah, how to probably think like a statistician? 1012 00:53:15,680 --> 00:53:17,319 Speaker 1: How statisticians likely think? 1013 00:53:18,239 --> 00:53:20,919 Speaker 4: Yeah, likely think or think likely? 1014 00:53:23,400 --> 00:53:25,600 Speaker 1: The likelihood of me finding a good joke is low. 1015 00:53:26,960 --> 00:53:32,480 Speaker 4: Yeah, we'll make that the null hypothesis. All right, And 1016 00:53:32,480 --> 00:53:35,319 Speaker 4: an interesting perspective though about how to think like a physicist. Now, 1017 00:53:35,400 --> 00:53:37,880 Speaker 4: let's talk to someone whose job it is to I 1018 00:53:37,920 --> 00:53:41,360 Speaker 4: guess reintroduce physicists out into the world, sort of like 1019 00:53:41,400 --> 00:53:45,560 Speaker 4: those wildlife experts who have to retrain animals to live 1020 00:53:45,560 --> 00:53:47,640 Speaker 4: in the wild. Is that is that kind of her job? 1021 00:53:48,920 --> 00:53:53,560 Speaker 1: Yeah, exactly, Or re educate prisoners who are emotion. 1022 00:53:53,480 --> 00:53:57,080 Speaker 4: Oh my goodness, I guess I could eat me a 1023 00:53:57,080 --> 00:54:00,880 Speaker 4: sort of like a prison. There are walls, towers, you know, 1024 00:54:00,960 --> 00:54:03,319 Speaker 4: small rooms where people are sit all day. 1025 00:54:04,600 --> 00:54:05,520 Speaker 1: The food is terrible. 1026 00:54:05,920 --> 00:54:07,799 Speaker 4: Do you have does your door have bars in it 1027 00:54:07,920 --> 00:54:12,880 Speaker 4: as well? And the ever sentence is like six or 1028 00:54:12,880 --> 00:54:13,359 Speaker 4: seven years? 1029 00:54:13,440 --> 00:54:15,399 Speaker 1: Right, Oh, I got a lifetime sentence over here. 1030 00:54:18,560 --> 00:54:23,080 Speaker 4: You did a capital discovery. All right, Well, we'll get 1031 00:54:23,120 --> 00:54:27,600 Speaker 4: to Daniel's interview with physicist Kathy Kopik about what physicists 1032 00:54:27,800 --> 00:54:31,440 Speaker 4: can do outside of physics. So let's dig into that. 1033 00:54:31,440 --> 00:54:33,280 Speaker 4: But first, let's take another quick break. 1034 00:54:37,760 --> 00:54:39,560 Speaker 1: When you pop a piece of cheese into your mouth, 1035 00:54:39,640 --> 00:54:42,800 Speaker 1: or enjoy a rich spoonful of Greek yogurt, you're probably 1036 00:54:42,840 --> 00:54:46,880 Speaker 1: not thinking about the environmental impact of each and every bite. 1037 00:54:46,920 --> 00:54:49,520 Speaker 1: But the people in the dairy industry are US. 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Well, Daniel, 1089 00:57:40,240 --> 00:57:43,280 Speaker 4: you got to talk to another physicist who sort of 1090 00:57:43,320 --> 00:57:45,000 Speaker 4: does something else that's kind of interesting. 1091 00:57:45,160 --> 00:57:47,960 Speaker 1: Yeah. Kathy Kopeik is an old friend of mine. She 1092 00:57:48,000 --> 00:57:50,600 Speaker 1: and I did experimental particle physics together many years ago, 1093 00:57:50,680 --> 00:57:53,440 Speaker 1: but then she ventured out into the world instead of 1094 00:57:53,480 --> 00:57:56,640 Speaker 1: continuing in physics research, and for many years her job 1095 00:57:56,840 --> 00:58:01,160 Speaker 1: was to help people who have PhDs in physics find 1096 00:58:01,280 --> 00:58:04,720 Speaker 1: jobs outside of physics, mostly in data science and in 1097 00:58:04,800 --> 00:58:07,480 Speaker 1: machine learning industry, which has been gobbling up a lot 1098 00:58:07,480 --> 00:58:08,560 Speaker 1: of physics PhDs. 1099 00:58:08,840 --> 00:58:10,560 Speaker 4: Well does she do this for a company or is 1100 00:58:10,600 --> 00:58:11,720 Speaker 4: a consultant or what? 1101 00:58:12,120 --> 00:58:14,920 Speaker 1: Yeah, there's a company called Insight Data Science, which was 1102 00:58:14,920 --> 00:58:16,960 Speaker 1: like a boot camp. It would take people from physics, 1103 00:58:17,280 --> 00:58:19,360 Speaker 1: give them a little bit of an introduction into the 1104 00:58:19,440 --> 00:58:22,320 Speaker 1: tools of business or industry, or at least help them 1105 00:58:22,360 --> 00:58:24,840 Speaker 1: translate their experience so they knew how to talk about it. 1106 00:58:25,200 --> 00:58:27,320 Speaker 1: I find that one of the biggest barrier between fields 1107 00:58:27,440 --> 00:58:30,840 Speaker 1: is just vocabulary. You know, everybody talks about the same 1108 00:58:30,880 --> 00:58:32,920 Speaker 1: thing using different words, and so if you just learn 1109 00:58:33,000 --> 00:58:36,800 Speaker 1: to translate your work, your expertise into somebody else's language, 1110 00:58:36,880 --> 00:58:38,880 Speaker 1: you can help them understand how you might be useful 1111 00:58:38,920 --> 00:58:39,560 Speaker 1: to their company. 1112 00:58:40,880 --> 00:58:43,040 Speaker 4: Right, Right, you just have to say things like I 1113 00:58:43,120 --> 00:58:47,360 Speaker 4: worked on a model to understand the universe, and then 1114 00:58:47,440 --> 00:58:48,680 Speaker 4: all scientists will understand you. 1115 00:58:50,520 --> 00:58:54,240 Speaker 1: I'm going to circle back and connect with stakeholders so 1116 00:58:54,280 --> 00:58:57,520 Speaker 1: that we can maximize shareholder profit. Right, that's my attempt to. 1117 00:58:57,520 --> 00:59:03,160 Speaker 4: Speak corporate world. That's how you think they talk in 1118 00:59:03,240 --> 00:59:04,160 Speaker 4: corporate America. 1119 00:59:04,240 --> 00:59:06,200 Speaker 1: I mean based on the sitcoms I watch. I mean 1120 00:59:06,240 --> 00:59:07,240 Speaker 1: research I've done. Then? 1121 00:59:07,320 --> 00:59:11,520 Speaker 4: Yes, Uh? Is that part of thinking like a physicist 1122 00:59:11,640 --> 00:59:13,520 Speaker 4: is doing your research on TV and YouTube? 1123 00:59:14,960 --> 00:59:16,160 Speaker 1: That's just part of living man. 1124 00:59:18,440 --> 00:59:20,680 Speaker 4: Now, you said Kathy used to do that. What does 1125 00:59:20,720 --> 00:59:21,280 Speaker 4: she do now? 1126 00:59:21,520 --> 00:59:23,440 Speaker 1: Yeah? Now Kathy has a bunch of jobs. She's teaching 1127 00:59:23,480 --> 00:59:25,080 Speaker 1: at Berkeley and at Stanford, and she has her own 1128 00:59:25,080 --> 00:59:28,880 Speaker 1: consulting company helping people find physicists to work in their teams. 1129 00:59:29,160 --> 00:59:31,720 Speaker 4: All right, well, here is Daniel's interview with doctor Kathy 1130 00:59:31,760 --> 00:59:34,840 Speaker 4: Kopeck on how to think like a physicists and how 1131 00:59:34,840 --> 00:59:36,800 Speaker 4: to get a job as a physicist, or how to 1132 00:59:36,800 --> 00:59:38,480 Speaker 4: pretend you're not a physicist to get a job. Is 1133 00:59:38,520 --> 00:59:39,120 Speaker 4: that is that? 1134 00:59:39,560 --> 00:59:42,680 Speaker 1: Yeah? Yeah, to get a non physics job if you 1135 00:59:42,720 --> 00:59:43,520 Speaker 1: are a physicist. 1136 00:59:43,760 --> 00:59:44,320 Speaker 4: There you go. 1137 00:59:45,840 --> 00:59:48,320 Speaker 1: All right. So then it's my great pleasure to introduce 1138 00:59:48,360 --> 00:59:52,400 Speaker 1: to the podcast my friend and colleague, doctor Kathy Copik. Kathy, 1139 00:59:52,400 --> 00:59:53,840 Speaker 1: thanks very much for joining us today. 1140 00:59:54,400 --> 00:59:56,120 Speaker 11: Oh thanks so much. I'm really excited. 1141 00:59:56,600 --> 00:59:58,600 Speaker 1: Tell us a little bit about who you are, what 1142 00:59:58,760 --> 01:00:01,760 Speaker 1: your background is. You have a special and unusual journey. 1143 01:00:02,440 --> 01:00:06,600 Speaker 11: Oh yeah, thanks sure. So I was a physicist and am 1144 01:00:06,600 --> 01:00:08,200 Speaker 11: a physicist. I don't know if we talk in the 1145 01:00:08,200 --> 01:00:11,920 Speaker 11: past or present tense, but I worked in experimental particle 1146 01:00:11,920 --> 01:00:16,600 Speaker 11: physicists for a long time, first actually in California and 1147 01:00:16,720 --> 01:00:20,680 Speaker 11: b Bar, then outside Chicago on the CDF experiment at Formulab. 1148 01:00:21,000 --> 01:00:24,200 Speaker 11: Then I was at CERN for a long time, as 1149 01:00:24,240 --> 01:00:28,720 Speaker 11: were you, working on the Atlas experiment. With Columbia and 1150 01:00:28,760 --> 01:00:30,960 Speaker 11: then with Berkeley. So I just I was in physics 1151 01:00:31,000 --> 01:00:35,520 Speaker 11: for a long time, studying the smallest things, and then 1152 01:00:35,640 --> 01:00:38,280 Speaker 11: I worked in the last ten years a lot on 1153 01:00:38,760 --> 01:00:43,320 Speaker 11: helping teams outside of academia think about how they use 1154 01:00:43,440 --> 01:00:45,840 Speaker 11: data in lots of ways, and how they hire their teams. 1155 01:00:45,920 --> 01:00:48,959 Speaker 11: I worked for about seven years at the Insight Data 1156 01:00:49,000 --> 01:00:52,040 Speaker 11: Science Fellows Program, working with a lot of scientists making 1157 01:00:52,040 --> 01:00:56,080 Speaker 11: a transition from working in science to working in tech 1158 01:00:56,120 --> 01:01:00,600 Speaker 11: in business, and worked with literally thousands of people making 1159 01:01:00,640 --> 01:01:04,160 Speaker 11: career transitions to literally hundreds of companies. And now I 1160 01:01:04,200 --> 01:01:07,520 Speaker 11: work as a consultant Fieldwork partners with a friend and 1161 01:01:07,560 --> 01:01:10,880 Speaker 11: we help teams do the same kind of things as consultants. 1162 01:01:11,080 --> 01:01:13,480 Speaker 1: So this may seem like an obvious question, but why 1163 01:01:13,520 --> 01:01:16,320 Speaker 1: are people making a transition? You're getting a PhD in 1164 01:01:16,320 --> 01:01:19,080 Speaker 1: particle physics, You're studying the secrets of the universe. Why 1165 01:01:19,120 --> 01:01:22,440 Speaker 1: are people then going to work for healthcare companies or whatever? 1166 01:01:23,040 --> 01:01:26,880 Speaker 11: Sure, yeah, I say two main reasons. One is genuine interest. 1167 01:01:26,960 --> 01:01:29,560 Speaker 11: You know, people are excited about and curious about lots 1168 01:01:29,560 --> 01:01:31,320 Speaker 11: of things. It's one of the things that drives them 1169 01:01:31,360 --> 01:01:34,400 Speaker 11: to be scientists in the first place. And I talked 1170 01:01:34,440 --> 01:01:37,720 Speaker 11: to lots of people who are interviewing with our programs 1171 01:01:39,160 --> 01:01:42,040 Speaker 11: to make that transition, and people were like, you know, 1172 01:01:42,040 --> 01:01:44,040 Speaker 11: I've done this thing for a long time and I 1173 01:01:44,080 --> 01:01:46,400 Speaker 11: really like doing it, and now I'm interested in doing 1174 01:01:46,400 --> 01:01:49,520 Speaker 11: something else, and so I think there is definitely genuine 1175 01:01:49,600 --> 01:01:53,280 Speaker 11: interest and curiosity about what it's like. And then I 1176 01:01:53,320 --> 01:01:55,320 Speaker 11: think on the other side, you know, the job market 1177 01:01:55,400 --> 01:01:58,760 Speaker 11: for academics is very hard getting that next position, that 1178 01:01:58,880 --> 01:02:02,960 Speaker 11: next position. Both it's very challenging. There's fewer and fewer 1179 01:02:02,960 --> 01:02:06,880 Speaker 11: positions at every level, and so naturally people have to 1180 01:02:06,920 --> 01:02:10,919 Speaker 11: exit academia, and also there's often fewer choice, like less 1181 01:02:11,000 --> 01:02:12,960 Speaker 11: choice at each level, so you know where you're going 1182 01:02:13,000 --> 01:02:14,640 Speaker 11: to live, what you're going to work on, who you're 1183 01:02:14,640 --> 01:02:17,280 Speaker 11: going to work with. Getting those positions is pretty tough, 1184 01:02:17,440 --> 01:02:19,400 Speaker 11: and so not just in physics, but in all fields 1185 01:02:19,440 --> 01:02:25,360 Speaker 11: across academia. People transition out after their undergrad after their PhD, 1186 01:02:25,640 --> 01:02:28,240 Speaker 11: after post docs, and sometimes at the faculty level as well. 1187 01:02:29,120 --> 01:02:31,800 Speaker 1: So we're always telling our students, hey, come to a 1188 01:02:31,840 --> 01:02:34,360 Speaker 1: PhD in physics because you're going to learn important skills 1189 01:02:34,360 --> 01:02:36,640 Speaker 1: about thinking and you're going to train yourself to be 1190 01:02:37,080 --> 01:02:40,640 Speaker 1: a smart person. And those skills are broadly applicable. And 1191 01:02:40,720 --> 01:02:42,720 Speaker 1: I've never worked outside of academia, so I don't know 1192 01:02:42,720 --> 01:02:45,080 Speaker 1: if I've been lying to people. Tell me, have I 1193 01:02:45,120 --> 01:02:48,360 Speaker 1: been lying to people? What skills do physics PhDs learn 1194 01:02:48,680 --> 01:02:51,120 Speaker 1: that are actually useful outside of particle physics? 1195 01:02:51,560 --> 01:02:54,920 Speaker 11: Sure? Sure, I do not think you are lying to people. 1196 01:02:54,960 --> 01:02:58,160 Speaker 11: I do think those skills are genuinely useful, and you 1197 01:02:58,160 --> 01:03:00,040 Speaker 11: can tell when you see where people go on to 1198 01:03:00,080 --> 01:03:02,640 Speaker 11: work after they've been in physics a lot of times 1199 01:03:02,640 --> 01:03:06,760 Speaker 11: in physics, and also that's from other places. The skills 1200 01:03:06,760 --> 01:03:11,080 Speaker 11: that people learn, I think there's three main things. The 1201 01:03:11,120 --> 01:03:13,800 Speaker 11: first one is just trying to figure out how to 1202 01:03:13,800 --> 01:03:18,160 Speaker 11: break a problem into smaller problems and questions, thinking about like, Okay, 1203 01:03:18,200 --> 01:03:21,080 Speaker 11: there's this big question we have, like what's the smallest 1204 01:03:21,120 --> 01:03:23,120 Speaker 11: thing in the universe, the thing that both you and 1205 01:03:23,160 --> 01:03:25,680 Speaker 11: I worked on and so have the big question? But 1206 01:03:25,720 --> 01:03:27,720 Speaker 11: then okay, how do I break that down into things 1207 01:03:27,760 --> 01:03:30,280 Speaker 11: that can be measured or things that we can write 1208 01:03:30,480 --> 01:03:34,680 Speaker 11: a theoretical model for. So breaking big questions into small 1209 01:03:34,760 --> 01:03:37,800 Speaker 11: questions it's a really important skill if you want to 1210 01:03:37,800 --> 01:03:40,320 Speaker 11: ask questions about the universe, but also if you want 1211 01:03:40,360 --> 01:03:44,280 Speaker 11: to ask questions about a business, or you know, how 1212 01:03:44,760 --> 01:03:46,640 Speaker 11: how many beds in a hospital are likely to be 1213 01:03:46,680 --> 01:03:49,360 Speaker 11: available on a given day given the procedures and things 1214 01:03:49,360 --> 01:03:52,160 Speaker 11: that are coming up, and how uncertain is it that 1215 01:03:52,360 --> 01:03:54,960 Speaker 11: people will get discharged on a certain day if you're 1216 01:03:54,960 --> 01:03:57,600 Speaker 11: trying to build a model of anything, not just in 1217 01:03:57,720 --> 01:04:00,560 Speaker 11: science but also in the real world, breaking big problem 1218 01:04:00,560 --> 01:04:03,000 Speaker 11: into small questions is a big, big skill. 1219 01:04:03,320 --> 01:04:05,080 Speaker 1: Let me drill into that a little bit. I understand 1220 01:04:05,160 --> 01:04:06,920 Speaker 1: it's important to know, like how to get started on 1221 01:04:06,960 --> 01:04:09,160 Speaker 1: a problem. You're working for a company and they give 1222 01:04:09,160 --> 01:04:11,120 Speaker 1: you this project. They're like, build us this widget that 1223 01:04:11,160 --> 01:04:13,120 Speaker 1: does that thing, and you need to know what to 1224 01:04:13,160 --> 01:04:15,680 Speaker 1: do on day one so that after day ninety you're there. 1225 01:04:16,440 --> 01:04:19,760 Speaker 1: Why is that something that physicists in particular are good at, Like, 1226 01:04:20,240 --> 01:04:22,680 Speaker 1: how does studying the nature of the universe make you 1227 01:04:22,760 --> 01:04:25,560 Speaker 1: good at learning how to break down problems? 1228 01:04:26,120 --> 01:04:28,720 Speaker 11: Yeah, a lot of the things that physicists are good 1229 01:04:28,720 --> 01:04:31,520 Speaker 11: at are think scientists in general are good at. I'm 1230 01:04:31,640 --> 01:04:35,520 Speaker 11: asking a question breaking it into problems, But physics in particular, 1231 01:04:35,920 --> 01:04:39,480 Speaker 11: I think both people who are drawn to physics and 1232 01:04:39,600 --> 01:04:43,760 Speaker 11: physics education reinforce the same thing, which is not just 1233 01:04:43,800 --> 01:04:46,720 Speaker 11: being a little bit curious, but being like really curious. 1234 01:04:48,360 --> 01:04:52,080 Speaker 11: You know, they're not just stopping at some level that's 1235 01:04:52,200 --> 01:04:56,120 Speaker 11: like a service level or where there's maybe approximations or things. 1236 01:04:56,160 --> 01:04:59,720 Speaker 11: You're like really continuing to either you personally because that's 1237 01:04:59,760 --> 01:05:02,520 Speaker 11: how you you think about the world, or in your 1238 01:05:02,640 --> 01:05:06,440 Speaker 11: education working with your teachers and mentors, are like really 1239 01:05:06,600 --> 01:05:10,320 Speaker 11: really really drilling down to these questions, to the really 1240 01:05:10,360 --> 01:05:14,040 Speaker 11: basic pieces of it. And I think that is unique 1241 01:05:14,040 --> 01:05:17,120 Speaker 11: to physics. It's you know, the people who study physics 1242 01:05:17,160 --> 01:05:20,320 Speaker 11: have chosen to kind of like continue down that path 1243 01:05:20,360 --> 01:05:23,520 Speaker 11: of questions to where you know, there's things are not 1244 01:05:23,600 --> 01:05:29,000 Speaker 11: even alive anymore. You're studying one atom, or studying how 1245 01:05:29,080 --> 01:05:35,400 Speaker 11: galaxies form, or some like very complicated basic question about 1246 01:05:35,440 --> 01:05:38,960 Speaker 11: the universe. So I think it's true everybody takes a 1247 01:05:39,040 --> 01:05:41,400 Speaker 11: question and breaks it into smaller questions in science, but 1248 01:05:41,480 --> 01:05:44,600 Speaker 11: in physics really really trying to get to the most 1249 01:05:44,640 --> 01:05:47,000 Speaker 11: basic things about how the world works. 1250 01:05:47,080 --> 01:05:49,560 Speaker 1: Right, all right? So I interrupted you. You were telling us 1251 01:05:49,880 --> 01:05:52,480 Speaker 1: the good things that physicists learned, and number one is 1252 01:05:52,520 --> 01:05:54,720 Speaker 1: breaking things into pieces, and number two was. 1253 01:05:54,800 --> 01:05:58,000 Speaker 11: Breaking things into pieces. Number two, I think, especially in 1254 01:05:58,040 --> 01:06:03,440 Speaker 11: experimental physics, working with very large general purpose data sets 1255 01:06:04,560 --> 01:06:06,480 Speaker 11: and a lot of parts of science. You know, every 1256 01:06:06,520 --> 01:06:09,520 Speaker 11: experimental science people have data sets. Sometimes they're very large, 1257 01:06:10,120 --> 01:06:13,880 Speaker 11: but a lot of scientists create those data sets themselves 1258 01:06:14,320 --> 01:06:16,400 Speaker 11: in a smaller group, so they have you know, they're 1259 01:06:16,400 --> 01:06:19,760 Speaker 11: trying to study one thing about how a certain bacteria 1260 01:06:20,040 --> 01:06:23,000 Speaker 11: does something, or you know, in their own lab, and 1261 01:06:23,040 --> 01:06:25,720 Speaker 11: they kind of know, oh, maybe the data from July 1262 01:06:25,840 --> 01:06:27,960 Speaker 11: is no good because the temperature was off or something. 1263 01:06:28,040 --> 01:06:31,560 Speaker 11: You know, they know the data often better because they 1264 01:06:31,680 --> 01:06:35,400 Speaker 11: created it. In physics, especially in experimental particle physics where 1265 01:06:35,440 --> 01:06:37,800 Speaker 11: we both worked, but also in astrophysics and lots of 1266 01:06:37,840 --> 01:06:41,680 Speaker 11: other areas of physics, people have these very collaborative general 1267 01:06:41,680 --> 01:06:45,000 Speaker 11: purpose data sets that are meant not just to answer 1268 01:06:45,000 --> 01:06:48,200 Speaker 11: one question, but you can ask so many questions from them. 1269 01:06:48,520 --> 01:06:51,920 Speaker 11: And they're messy. They're built, these detectors that are built, 1270 01:06:51,960 --> 01:06:55,640 Speaker 11: and we have problems, some parts not working. Maybe that's 1271 01:06:55,640 --> 01:06:59,400 Speaker 11: showing up in some initial variables, also in some calculated 1272 01:06:59,480 --> 01:07:02,360 Speaker 11: variables on the road. You have to make corrections. Working 1273 01:07:02,360 --> 01:07:05,680 Speaker 11: with that kind of general purpose data is a real 1274 01:07:05,720 --> 01:07:09,600 Speaker 11: skill because that real world data that you might study 1275 01:07:09,840 --> 01:07:12,640 Speaker 11: if you're working at a business or nonprofit or asking 1276 01:07:12,640 --> 01:07:17,640 Speaker 11: some questions about non academic data very similar to So 1277 01:07:17,680 --> 01:07:20,920 Speaker 11: that's a skill I think people learn in physics. And 1278 01:07:20,960 --> 01:07:23,160 Speaker 11: then a third one I would say, is this collaboration 1279 01:07:24,000 --> 01:07:27,000 Speaker 11: working in. You know, not all collaborations are as big 1280 01:07:27,040 --> 01:07:29,640 Speaker 11: as the ones that we worked on most or not, 1281 01:07:30,160 --> 01:07:33,680 Speaker 11: but working in everybody who's working in physics and in 1282 01:07:33,720 --> 01:07:37,280 Speaker 11: science is really trying to figure out what's already been done. 1283 01:07:37,600 --> 01:07:40,480 Speaker 11: Who has domain knowledge that might help me figure out 1284 01:07:40,520 --> 01:07:42,440 Speaker 11: the piece of it that I'm working on. How do 1285 01:07:42,520 --> 01:07:44,920 Speaker 11: I share what I'm working on in a way that 1286 01:07:45,160 --> 01:07:48,240 Speaker 11: can make sense to build some collaboration. How do I 1287 01:07:48,280 --> 01:07:50,480 Speaker 11: share my results back? How do I write about and 1288 01:07:50,520 --> 01:07:53,560 Speaker 11: speak about what I learned in a way that's going 1289 01:07:53,640 --> 01:07:57,200 Speaker 11: to help advance the research on this question? So all 1290 01:07:57,240 --> 01:07:58,960 Speaker 11: of those I think are really. 1291 01:07:58,800 --> 01:08:02,440 Speaker 1: Important, standing like what it's like to think like a physicist. 1292 01:08:02,840 --> 01:08:05,960 Speaker 1: I think one thing that's helpful is understanding where physicists 1293 01:08:06,000 --> 01:08:08,240 Speaker 1: find their skills useful. So you told us the kind 1294 01:08:08,240 --> 01:08:10,920 Speaker 1: of skills we learn. But where do people who have 1295 01:08:11,040 --> 01:08:15,080 Speaker 1: been trained in particle physics end up making impacts in 1296 01:08:15,160 --> 01:08:18,360 Speaker 1: the world outside of particle physics? Where are these skills helpful? 1297 01:08:18,920 --> 01:08:22,559 Speaker 11: Yeah? I think really everywhere, And I'm not just like 1298 01:08:23,080 --> 01:08:27,000 Speaker 11: trying to make it seem just everywhere, but in all 1299 01:08:27,040 --> 01:08:29,479 Speaker 11: the kinds of tech companies that you can think of 1300 01:08:29,560 --> 01:08:32,000 Speaker 11: that are working today, like people are doing interesting work. 1301 01:08:32,160 --> 01:08:36,439 Speaker 11: Also small places, nonprofits I mentioned initially. I mentioned this, 1302 01:08:36,640 --> 01:08:39,679 Speaker 11: like people working at a hospital to try to figure 1303 01:08:39,720 --> 01:08:43,479 Speaker 11: out how to build a system that helps predict when 1304 01:08:43,520 --> 01:08:45,559 Speaker 11: patients are going to be coming in or not. People 1305 01:08:45,600 --> 01:08:48,720 Speaker 11: are working in pharmaceuticals, just really in every area I 1306 01:08:48,760 --> 01:08:52,200 Speaker 11: think people are working. I mean, yeah, there's there's so 1307 01:08:52,320 --> 01:08:57,040 Speaker 11: many experiment particle physicists to so many of us that 1308 01:08:57,520 --> 01:09:00,200 Speaker 11: people go in and people are driven and curious to 1309 01:09:00,240 --> 01:09:04,479 Speaker 11: work on so many things that yeah, just lots of places. 1310 01:09:04,800 --> 01:09:07,519 Speaker 1: And you know, physics is very good broad training, but 1311 01:09:07,560 --> 01:09:10,240 Speaker 1: we're not learning everything when people go out into the 1312 01:09:10,240 --> 01:09:13,120 Speaker 1: world and try to work on actual practical problems with 1313 01:09:13,320 --> 01:09:15,759 Speaker 1: real deliverables and stuff. What are some sort of blind 1314 01:09:15,800 --> 01:09:18,800 Speaker 1: spots which some things that physicists don't learn that are 1315 01:09:18,920 --> 01:09:20,160 Speaker 1: useful in the rest of the world. 1316 01:09:20,960 --> 01:09:25,280 Speaker 11: Yeah, I think that all of those advantages, those superpowers 1317 01:09:25,320 --> 01:09:28,000 Speaker 11: that I talked about have some kind of reverse kryptonite, 1318 01:09:28,040 --> 01:09:31,519 Speaker 11: which is like being very curious and very detail oriented 1319 01:09:31,560 --> 01:09:33,720 Speaker 11: and driven to like get to the very bottom of 1320 01:09:33,760 --> 01:09:36,879 Speaker 11: the question is a good instinct. In physics, it's important. 1321 01:09:37,080 --> 01:09:41,600 Speaker 11: But sometimes in the business world, you don't have the 1322 01:09:41,680 --> 01:09:45,000 Speaker 11: time or resources to like get really to the very 1323 01:09:45,040 --> 01:09:47,040 Speaker 11: bottom of something, and you have to kind of step 1324 01:09:47,080 --> 01:09:50,320 Speaker 11: back and make an approximation. Or maybe we're only going 1325 01:09:50,360 --> 01:09:51,800 Speaker 11: to run this thing for a week and we're going 1326 01:09:51,880 --> 01:09:53,240 Speaker 11: to get as far as we're going to get. But 1327 01:09:53,600 --> 01:09:55,040 Speaker 11: at the end, what we're trying to do is like 1328 01:09:55,160 --> 01:09:57,960 Speaker 11: recommend the next song for someone, or recommend the next 1329 01:09:58,400 --> 01:10:00,839 Speaker 11: for someone to watch. And so it actually it's okay 1330 01:10:01,320 --> 01:10:04,280 Speaker 11: if like we don't understand everything about this, and so 1331 01:10:04,360 --> 01:10:06,519 Speaker 11: sometimes taking that step back and being like, you know, 1332 01:10:06,600 --> 01:10:10,000 Speaker 11: this isn't a six month project or a six year project. 1333 01:10:10,040 --> 01:10:13,080 Speaker 11: This is like a six week project, and we're going 1334 01:10:13,120 --> 01:10:15,360 Speaker 11: to build something and we're gonna ship it and it's 1335 01:10:15,640 --> 01:10:17,200 Speaker 11: going to be good enough for that need, you know. 1336 01:10:17,240 --> 01:10:19,080 Speaker 11: And there are areas where that's true, and then there 1337 01:10:19,080 --> 01:10:22,839 Speaker 11: are areas, you know, where like in health and healthcare, 1338 01:10:22,880 --> 01:10:25,160 Speaker 11: where you don't want to make errors. And so I 1339 01:10:25,160 --> 01:10:28,599 Speaker 11: think people kind of through their personality might choose areas 1340 01:10:28,640 --> 01:10:32,960 Speaker 11: where it's okay to you know, recommend the next song 1341 01:10:33,000 --> 01:10:35,320 Speaker 11: for someone. They might not enjoy as much. Where it's 1342 01:10:35,360 --> 01:10:38,519 Speaker 11: not okay to recommend, you know, a medication to someone 1343 01:10:38,640 --> 01:10:41,080 Speaker 11: that's not the right fit for them, right if it's 1344 01:10:41,200 --> 01:10:43,160 Speaker 11: you know, and there's still would usually in a in a 1345 01:10:43,160 --> 01:10:45,040 Speaker 11: healthcare setting there would be a doctor that would be 1346 01:10:45,080 --> 01:10:48,280 Speaker 11: the prescriber. But if you have a tool that's very 1347 01:10:48,280 --> 01:10:52,080 Speaker 11: biased or making wrong predictions for something that's really important 1348 01:10:52,120 --> 01:10:55,599 Speaker 11: like healthcare, you know, there's less room for error. 1349 01:10:56,360 --> 01:10:59,000 Speaker 1: So you've helped a lot of people figure out how 1350 01:10:59,040 --> 01:11:02,280 Speaker 1: to go from particles to someplace in the real world 1351 01:11:02,320 --> 01:11:04,400 Speaker 1: where they can make a contribution. How do you do that? 1352 01:11:04,479 --> 01:11:06,960 Speaker 1: How do you like get to know somebody and figure out, 1353 01:11:07,000 --> 01:11:09,400 Speaker 1: like what are their strengths and weaknesses and how does 1354 01:11:09,400 --> 01:11:12,080 Speaker 1: it fit. I mean, you're basically like the Yinta of 1355 01:11:12,680 --> 01:11:15,719 Speaker 1: particle physics and jobs. But tell us about your process. 1356 01:11:15,840 --> 01:11:18,240 Speaker 11: Sure, sure, everybody is very different. That's one thing that 1357 01:11:18,320 --> 01:11:21,040 Speaker 11: I enjoy about it. So, you know, some people need 1358 01:11:21,080 --> 01:11:23,639 Speaker 11: to grow or change in one area where other folks 1359 01:11:23,640 --> 01:11:26,160 Speaker 11: it's very different for them. I think the first thing 1360 01:11:26,680 --> 01:11:29,160 Speaker 11: that I try to ask is what motivates people, what 1361 01:11:29,200 --> 01:11:31,799 Speaker 11: they're excited by. You know, some people are very excited 1362 01:11:31,840 --> 01:11:34,960 Speaker 11: by the impact in the real world and the people 1363 01:11:35,000 --> 01:11:36,920 Speaker 11: that might use or be helped by the thing they're 1364 01:11:36,920 --> 01:11:39,839 Speaker 11: working on. Other folks are very excited about the technical 1365 01:11:39,880 --> 01:11:43,240 Speaker 11: tools themselves, like getting to use the most advanced tools 1366 01:11:43,240 --> 01:11:46,719 Speaker 11: and models and getting to work on something technically very exciting. 1367 01:11:47,160 --> 01:11:50,920 Speaker 11: Other people are have worked very deeply and you know, 1368 01:11:51,000 --> 01:11:53,920 Speaker 11: worked ten years on one thing and are actually looking 1369 01:11:54,040 --> 01:11:56,599 Speaker 11: to do something more broad like they're oh, I want 1370 01:11:56,600 --> 01:11:59,280 Speaker 11: to learn about a lot of things. Some people love 1371 01:11:59,280 --> 01:12:01,240 Speaker 11: to interact with the lot of people. Some people want 1372 01:12:01,280 --> 01:12:03,040 Speaker 11: to be a little bit more like I kind of 1373 01:12:03,080 --> 01:12:05,080 Speaker 11: want to be given the thing and do my own thing. 1374 01:12:05,640 --> 01:12:08,880 Speaker 11: And so I think there's very different work for people 1375 01:12:08,920 --> 01:12:12,960 Speaker 11: depending on what they like and what they're interested in. 1376 01:12:13,280 --> 01:12:15,080 Speaker 11: And so once you know a little bit more about that, 1377 01:12:15,160 --> 01:12:17,599 Speaker 11: like what are the constraints around the kind of jobs 1378 01:12:17,640 --> 01:12:20,320 Speaker 11: that people are looking for, then I think it's easy 1379 01:12:20,600 --> 01:12:25,040 Speaker 11: to recommend specific like okay, well, and based on geography too, 1380 01:12:25,160 --> 01:12:27,600 Speaker 11: like there's just different kinds of jobs in different places 1381 01:12:27,840 --> 01:12:31,519 Speaker 11: in North America and the world, and so okay, well, 1382 01:12:31,560 --> 01:12:34,320 Speaker 11: for you, it sounds like you're excited about this and 1383 01:12:34,320 --> 01:12:38,519 Speaker 11: you're living here and these are your experiences helping people 1384 01:12:39,160 --> 01:12:43,080 Speaker 11: describe what they've done and what they want to do next. 1385 01:12:44,080 --> 01:12:47,400 Speaker 11: People usually don't need to build new skills. They have 1386 01:12:47,439 --> 01:12:49,920 Speaker 11: a lot of skills. It's just they need to have 1387 01:12:50,000 --> 01:12:54,559 Speaker 11: some kind of exploration of the space of available things, 1388 01:12:54,840 --> 01:12:58,360 Speaker 11: what they want, what they have, how they can describe 1389 01:12:58,360 --> 01:13:02,240 Speaker 11: what they've done, and maybe demonstrate it in a different way, 1390 01:13:02,400 --> 01:13:06,880 Speaker 11: you know, by talking about it differently. Those are the 1391 01:13:06,920 --> 01:13:08,080 Speaker 11: main things I think I would do. 1392 01:13:08,600 --> 01:13:10,800 Speaker 1: So I've seen a lot of physicists end up like 1393 01:13:10,920 --> 01:13:14,600 Speaker 1: on Wall Street or in data science. These seem to 1394 01:13:14,680 --> 01:13:18,280 Speaker 1: be places like where that community has an appetite for 1395 01:13:18,400 --> 01:13:21,320 Speaker 1: the like, oh, yeah, we like hiring physicists or whatever. Yeah, 1396 01:13:21,680 --> 01:13:24,120 Speaker 1: but tell us some other places where physicists might end 1397 01:13:24,200 --> 01:13:27,759 Speaker 1: up some you know, maybe unusual or bizarre places physics 1398 01:13:27,760 --> 01:13:28,840 Speaker 1: PhDs end up working in. 1399 01:13:29,080 --> 01:13:33,360 Speaker 11: Yeah, that's a good question. I do think people end 1400 01:13:33,439 --> 01:13:37,400 Speaker 11: up in a lot of places that basically anywhere where 1401 01:13:37,520 --> 01:13:42,719 Speaker 11: people are like building some models to help a system 1402 01:13:42,840 --> 01:13:45,879 Speaker 11: run better. So it could be you know, things education, 1403 01:13:46,200 --> 01:13:49,360 Speaker 11: educational software. People are trying to build ways to help 1404 01:13:49,439 --> 01:13:52,400 Speaker 11: kids learn to read and learn to do math. There's 1405 01:13:52,640 --> 01:13:55,960 Speaker 11: all kinds of games that people work on. Anything that 1406 01:13:56,040 --> 01:14:00,639 Speaker 11: you buy or sell clothes or you know, any sort 1407 01:14:00,640 --> 01:14:03,200 Speaker 11: of products any sort of recommendations for things that you're 1408 01:14:03,280 --> 01:14:06,000 Speaker 11: that people are working on. Anything in the healthcare industry. 1409 01:14:06,040 --> 01:14:09,720 Speaker 11: I talked about that a lot already. Anything in the 1410 01:14:09,960 --> 01:14:12,519 Speaker 11: kind of broad tech you see, there's a ton of 1411 01:14:13,760 --> 01:14:17,120 Speaker 11: work right now in AI, certainly large language models. A 1412 01:14:17,160 --> 01:14:19,479 Speaker 11: lot of people from physics are working on those tools 1413 01:14:19,560 --> 01:14:23,240 Speaker 11: at all the places you can imagine, there's really a 1414 01:14:23,280 --> 01:14:25,479 Speaker 11: lot of a lot of places. I can't think of 1415 01:14:25,560 --> 01:14:30,040 Speaker 11: one like fun especially funny, like, oh, here's one thing 1416 01:14:30,080 --> 01:14:33,360 Speaker 11: you can think of, but really in every area media, fashion, 1417 01:14:34,040 --> 01:14:36,000 Speaker 11: people are working in all sorts of areas. 1418 01:14:36,160 --> 01:14:39,479 Speaker 1: People working on like optimizing you know, underwear sizes and 1419 01:14:39,479 --> 01:14:42,599 Speaker 1: stuff like this. For sure, for sure, that's particle physics 1420 01:14:42,600 --> 01:14:43,000 Speaker 1: at work. 1421 01:14:43,800 --> 01:14:46,679 Speaker 11: That's right, it's funny and it's a joke. But it's 1422 01:14:46,720 --> 01:14:49,920 Speaker 11: also true that like I don't know, for me, finding 1423 01:14:49,920 --> 01:14:52,040 Speaker 11: clothes that fit is actually really nice. 1424 01:14:52,160 --> 01:14:54,360 Speaker 1: Yes, it's an important unsolved fund. You can make a 1425 01:14:54,400 --> 01:14:56,160 Speaker 1: real impact in people's lives. 1426 01:14:56,439 --> 01:14:58,320 Speaker 11: I mean, it's like a little bit silly, but it's 1427 01:14:58,320 --> 01:15:00,439 Speaker 11: also true that there's a lot of I think there's 1428 01:15:00,479 --> 01:15:03,200 Speaker 11: a lot of systems where people have just done the 1429 01:15:03,200 --> 01:15:05,760 Speaker 11: same thing forever and having a fresh take on it 1430 01:15:05,760 --> 01:15:06,519 Speaker 11: can be helpful. 1431 01:15:06,680 --> 01:15:08,960 Speaker 1: Yeah, everybody's got like their favorite pair of jeans or 1432 01:15:08,960 --> 01:15:10,720 Speaker 1: their favorite pair of underwear, and there's a reason they 1433 01:15:10,760 --> 01:15:14,920 Speaker 1: fit right. It feels good. So there's this lore going 1434 01:15:14,960 --> 01:15:17,240 Speaker 1: around that I hear a lot that one of the 1435 01:15:17,240 --> 01:15:21,000 Speaker 1: reasons behind the two thousand and eight financial collapse was 1436 01:15:21,560 --> 01:15:23,760 Speaker 1: that Wall Street went a little bit crazy with its 1437 01:15:23,800 --> 01:15:26,920 Speaker 1: modeling and that there were these crazy quants and most 1438 01:15:26,960 --> 01:15:30,000 Speaker 1: of them were ex physicists who didn't really understand the 1439 01:15:30,040 --> 01:15:32,560 Speaker 1: system and just like up A, wrote a bunch of 1440 01:15:32,600 --> 01:15:36,680 Speaker 1: code that went crazy and destroyed people's lives. So what 1441 01:15:36,720 --> 01:15:38,960 Speaker 1: do they have to say that did this just call 1442 01:15:39,000 --> 01:15:41,080 Speaker 1: the cause the financial collapse or not? 1443 01:15:42,400 --> 01:15:49,200 Speaker 11: Probably not alone. I'll say that the do you think 1444 01:15:49,240 --> 01:15:54,000 Speaker 11: there's a superpower kryptonite that physicists are very interested in, 1445 01:15:54,200 --> 01:15:56,839 Speaker 11: you know, going down to their root causes, the basic 1446 01:15:58,439 --> 01:16:00,000 Speaker 11: How do you take this problem and break it into 1447 01:16:00,080 --> 01:16:03,679 Speaker 11: the basic parts? And I think that the kryptonite version 1448 01:16:03,680 --> 01:16:05,559 Speaker 11: of that is like thinking that you can do that 1449 01:16:05,680 --> 01:16:09,880 Speaker 11: in any field, for any topic without necessarily consulting and 1450 01:16:09,960 --> 01:16:13,639 Speaker 11: learning about the domaining knowledge of the practitioners or people 1451 01:16:13,640 --> 01:16:16,800 Speaker 11: that have worked in that area. There's a famous data 1452 01:16:16,840 --> 01:16:19,920 Speaker 11: science person, Drew Conway who used to say, physicists, we're 1453 01:16:19,960 --> 01:16:21,800 Speaker 11: like kind of like will to beasts that would like 1454 01:16:21,920 --> 01:16:25,519 Speaker 11: run into an area that seems interesting, like biophysics. Right, 1455 01:16:25,520 --> 01:16:28,160 Speaker 11: it's like, oh, there's something interesting there. As here comes 1456 01:16:28,240 --> 01:16:30,400 Speaker 11: a lot of old, you know, ex physicists who are like, 1457 01:16:30,760 --> 01:16:33,000 Speaker 11: we'll solve all the problems. And so when I would 1458 01:16:33,040 --> 01:16:36,200 Speaker 11: give talks to physicis, I would say, don't be a wildbeast, like, 1459 01:16:36,320 --> 01:16:40,400 Speaker 11: don't run into air area to a new area. So 1460 01:16:40,600 --> 01:16:42,800 Speaker 11: these maybe these two thousand and eight physicists are kind 1461 01:16:42,840 --> 01:16:44,920 Speaker 11: of just like I know, I'll break down this problem 1462 01:16:44,920 --> 01:16:47,200 Speaker 11: into these parts, and look what I'm doing, isn't it cool? 1463 01:16:47,680 --> 01:16:51,280 Speaker 11: But if there was a little bit more domain knowledge 1464 01:16:51,400 --> 01:16:54,800 Speaker 11: or thought around how could this go wrong? How might 1465 01:16:54,840 --> 01:16:58,680 Speaker 11: this affect people who? Why might we not do this? 1466 01:17:00,560 --> 01:17:02,839 Speaker 11: Could have avoided some bad outcomes? 1467 01:17:02,960 --> 01:17:05,439 Speaker 1: All right, So maybe we're not totally guilty, just partially. 1468 01:17:06,240 --> 01:17:06,439 Speaker 11: Yeah. 1469 01:17:07,040 --> 01:17:09,880 Speaker 1: So a lot of our audience are folks who like 1470 01:17:09,960 --> 01:17:12,400 Speaker 1: physics and like thinking about physics and have been listening 1471 01:17:12,439 --> 01:17:15,000 Speaker 1: to the pod and learning to think like a physicist 1472 01:17:15,080 --> 01:17:18,080 Speaker 1: and applying you know, that mental model to questions about 1473 01:17:18,080 --> 01:17:20,760 Speaker 1: the universe. But what would be your advice for somebody 1474 01:17:20,800 --> 01:17:23,760 Speaker 1: out there who wants to take advantage of this way 1475 01:17:23,760 --> 01:17:26,519 Speaker 1: of thinking, somebody who's not necessarily trained as a physicist 1476 01:17:26,520 --> 01:17:29,439 Speaker 1: but wants to learn to think like a physicist. What 1477 01:17:29,439 --> 01:17:32,440 Speaker 1: would be your advice for learning to think that way? 1478 01:17:32,520 --> 01:17:35,240 Speaker 11: Yeah, I think there's this. I'm sure you talk about 1479 01:17:35,520 --> 01:17:40,320 Speaker 11: Drake's equation, which is used for thinking about where extraterrestrial 1480 01:17:40,320 --> 01:17:41,519 Speaker 11: life might be in the Milky Way? 1481 01:17:41,600 --> 01:17:41,720 Speaker 1: Right? 1482 01:17:41,800 --> 01:17:43,720 Speaker 11: Is that right? You probably know much more of that. 1483 01:17:43,920 --> 01:17:46,040 Speaker 11: So that's the thing where you kind of are taking 1484 01:17:46,080 --> 01:17:49,439 Speaker 11: these pieces. Anybody can look up the Drake equation or 1485 01:17:49,479 --> 01:17:53,240 Speaker 11: Drake's equation and taking these pieces and trying to put 1486 01:17:53,240 --> 01:17:55,240 Speaker 11: it together to get one answer. And I went to 1487 01:17:55,320 --> 01:17:57,760 Speaker 11: a business class where people were talking about using this 1488 01:17:57,960 --> 01:18:01,520 Speaker 11: same sort of thing to model businesses or other processes 1489 01:18:01,680 --> 01:18:05,200 Speaker 11: where it's just trying to think about anybody can think 1490 01:18:05,240 --> 01:18:09,160 Speaker 11: about what are the parts that come together to h 1491 01:18:09,760 --> 01:18:13,559 Speaker 11: to create some answer or some prediction. And so just 1492 01:18:13,560 --> 01:18:16,960 Speaker 11: take thinking about that. Breaking something up into things that 1493 01:18:17,000 --> 01:18:19,640 Speaker 11: you can measure individually or you can think about individually, 1494 01:18:20,120 --> 01:18:22,840 Speaker 11: can really help solve a problem, whether it's a science problem, 1495 01:18:23,000 --> 01:18:24,839 Speaker 11: business problem, any kind of problems. 1496 01:18:24,880 --> 01:18:26,680 Speaker 1: All right, And so then last question a bit of 1497 01:18:26,680 --> 01:18:29,919 Speaker 1: a personal one. What do you miss most about actively 1498 01:18:29,960 --> 01:18:32,240 Speaker 1: working in physics? I would say about being a physicist, 1499 01:18:32,240 --> 01:18:34,479 Speaker 1: because I think you're always a physicist once you're trying, 1500 01:18:34,920 --> 01:18:37,320 Speaker 1: like once a Jedi, always in Jedi. But what do 1501 01:18:37,320 --> 01:18:40,920 Speaker 1: you miss most about like working on particle physics other 1502 01:18:41,000 --> 01:18:43,679 Speaker 1: than working with me? Obviously I was going to say. 1503 01:18:46,000 --> 01:18:50,920 Speaker 11: I mean, you're joking, but I think I really really did. 1504 01:18:52,080 --> 01:18:56,479 Speaker 11: There's a very special, fun, exciting environment of being at 1505 01:18:56,560 --> 01:18:59,800 Speaker 11: the lab in these big experiments and both that you 1506 01:18:59,800 --> 01:19:03,160 Speaker 11: know at SLACK in California, at Fermi Lab, Brooke Gaven 1507 01:19:03,320 --> 01:19:07,160 Speaker 11: at CERN that these labs just it's really literally people 1508 01:19:07,160 --> 01:19:10,080 Speaker 11: from all over the world and having lunch together, and 1509 01:19:10,479 --> 01:19:15,080 Speaker 11: the big cafeteria. Cerns called our One restaurant one a 1510 01:19:15,280 --> 01:19:17,880 Speaker 11: very creative name. I don't know if it still is. 1511 01:19:17,880 --> 01:19:21,439 Speaker 11: It's not named after someone now, is it still our one? Yeah? 1512 01:19:21,479 --> 01:19:24,559 Speaker 11: So our one. So if you're there for lunch or 1513 01:19:24,600 --> 01:19:26,920 Speaker 11: for coffee or the end of the day, it's just 1514 01:19:27,040 --> 01:19:29,960 Speaker 11: really fun to run into so many people that you've 1515 01:19:30,000 --> 01:19:32,479 Speaker 11: worked with over your whole career, people who are getting 1516 01:19:32,520 --> 01:19:35,559 Speaker 11: into the field, people who are very senior. You never 1517 01:19:35,600 --> 01:19:39,200 Speaker 11: know who's going to be there, just having some food, 1518 01:19:39,439 --> 01:19:42,080 Speaker 11: drinking coffee, and getting to talk to people about what 1519 01:19:42,080 --> 01:19:44,880 Speaker 11: they're working on and also what they're doing and how 1520 01:19:44,920 --> 01:19:49,160 Speaker 11: they are. It's very very fund memories of hanging out 1521 01:19:49,200 --> 01:19:53,720 Speaker 11: there with with all sorts of people and yeah, no, 1522 01:19:53,760 --> 01:19:55,960 Speaker 11: it's a great time. So I would say just missing 1523 01:19:56,400 --> 01:20:00,680 Speaker 11: being with with with all the people that we used 1524 01:20:00,720 --> 01:20:03,080 Speaker 11: to work with and getting to meet new people. That's 1525 01:20:03,240 --> 01:20:05,800 Speaker 11: a really truly international environment too, really fun. 1526 01:20:06,000 --> 01:20:09,320 Speaker 1: It is fun to hear conversations in so many different languages. 1527 01:20:09,840 --> 01:20:12,800 Speaker 1: I like running into the same really old Nobel Prize 1528 01:20:12,840 --> 01:20:15,200 Speaker 1: winners over and over again, introducing myself every single time 1529 01:20:15,200 --> 01:20:17,000 Speaker 1: because they don't remember me because they're like one hundred 1530 01:20:17,040 --> 01:20:20,160 Speaker 1: and fifty years old. And I also remember one of 1531 01:20:20,160 --> 01:20:22,800 Speaker 1: the first times I was at our one and you 1532 01:20:22,880 --> 01:20:25,519 Speaker 1: had some special trick for making an iced coffee. When 1533 01:20:25,600 --> 01:20:27,439 Speaker 1: you showed it to me and Katrina and for the 1534 01:20:27,479 --> 01:20:29,000 Speaker 1: rest of the summer we were like, oh, let's get 1535 01:20:29,000 --> 01:20:32,200 Speaker 1: a Kathy. We called it a Kathy and pacino a coppacina. 1536 01:20:32,360 --> 01:20:34,720 Speaker 4: That's right, Yeah, yeah, I did. 1537 01:20:34,760 --> 01:20:38,160 Speaker 11: I've made up created a TGI Fridays. I don't know 1538 01:20:38,160 --> 01:20:41,839 Speaker 11: if you're looking for sponsorships. Daniel Tgi Fridays. The restaurant 1539 01:20:41,840 --> 01:20:46,439 Speaker 11: when I was a server there, created the copacina Copacino delicious. 1540 01:20:46,479 --> 01:20:47,880 Speaker 1: Thank you got us through that summer. 1541 01:20:48,240 --> 01:20:50,719 Speaker 11: Yeah, they don't have They didn't have cappuccino machines that started. 1542 01:20:50,760 --> 01:20:54,439 Speaker 11: They had espresso machines, but no, no cappuccino machines, so 1543 01:20:54,439 --> 01:20:55,360 Speaker 11: you got to figure it out. 1544 01:20:55,880 --> 01:20:58,280 Speaker 1: All right. Well, thanks very much for sharing with us 1545 01:20:58,400 --> 01:21:00,000 Speaker 1: how to think like a physicist and how to drink 1546 01:21:00,080 --> 01:21:02,840 Speaker 1: coffee like a physicist. Really appreciate it. 1547 01:21:03,240 --> 01:21:05,599 Speaker 11: We'll put the recipe people can. 1548 01:21:06,120 --> 01:21:11,200 Speaker 1: That's in the show notes show notes exactly. All right, 1549 01:21:11,240 --> 01:21:11,840 Speaker 1: thanks Kathy. 1550 01:21:12,280 --> 01:21:15,160 Speaker 4: All right, interesting talk there, Daniel. It seems like she's 1551 01:21:15,320 --> 01:21:17,040 Speaker 4: basically saying we all have skills. 1552 01:21:19,080 --> 01:21:20,599 Speaker 1: Everybody's skills are different at least. 1553 01:21:20,640 --> 01:21:20,840 Speaker 6: Yeah. 1554 01:21:20,840 --> 01:21:22,720 Speaker 1: I think she probably aligns with you to think that, 1555 01:21:22,760 --> 01:21:26,559 Speaker 1: like scientists are all curious thinkers and mental model builders, 1556 01:21:26,560 --> 01:21:29,080 Speaker 1: and not even all physicists are the same. We all 1557 01:21:29,120 --> 01:21:31,719 Speaker 1: think differently and enjoy different parts of the process. 1558 01:21:32,520 --> 01:21:35,760 Speaker 4: M and that can help you get a job, right, 1559 01:21:36,080 --> 01:21:38,479 Speaker 4: because these are all skills that we could all use 1560 01:21:38,560 --> 01:21:39,120 Speaker 4: in every field. 1561 01:21:39,160 --> 01:21:41,760 Speaker 1: Probably, yeah, exactly. And so in the end, thinking like 1562 01:21:41,800 --> 01:21:44,559 Speaker 1: a physicist is just like thinking like a scientist, being 1563 01:21:44,600 --> 01:21:47,920 Speaker 1: a curious person, trying to understand the world, being methodical 1564 01:21:48,120 --> 01:21:50,439 Speaker 1: about it, trying not to fool yourself with what the 1565 01:21:50,520 --> 01:21:51,240 Speaker 1: data is telling you. 1566 01:21:51,960 --> 01:21:55,720 Speaker 4: Yeah, and just trying to maximize your functionality to the stakeholders. 1567 01:21:57,520 --> 01:22:02,040 Speaker 1: Exactly, maximize shareholder revenue. 1568 01:22:01,960 --> 01:22:03,519 Speaker 4: Mike andmized physicists employment. 1569 01:22:06,120 --> 01:22:08,439 Speaker 1: Try not to cause any more financial collapses. Please. 1570 01:22:08,600 --> 01:22:12,120 Speaker 4: All right, Well, an interesting discussion about thinking like a scientist, 1571 01:22:12,120 --> 01:22:15,679 Speaker 4: thinking like a physicist. What are the commonalities and how 1572 01:22:15,800 --> 01:22:19,120 Speaker 4: things might be a little bit unique for people who 1573 01:22:19,120 --> 01:22:20,840 Speaker 4: pursue physics as a career. 1574 01:22:20,840 --> 01:22:23,040 Speaker 1: And for those of you out there not pursuing physics 1575 01:22:23,040 --> 01:22:25,960 Speaker 1: as a career but who have discovered a love for physics, 1576 01:22:26,040 --> 01:22:29,240 Speaker 1: keep doing it, keep thinking like a physicist or a scientist, 1577 01:22:29,280 --> 01:22:31,560 Speaker 1: and keep being curious about the world and trying to 1578 01:22:31,600 --> 01:22:33,920 Speaker 1: make the whole thing click together in your mind. 1579 01:22:34,080 --> 01:22:36,760 Speaker 4: Yeah, but mostly just keep thinking, please. 1580 01:22:37,840 --> 01:22:40,519 Speaker 1: And keep listening to the pod. Thanks everybody, We. 1581 01:22:40,560 --> 01:22:43,360 Speaker 4: Hope you enjoyed that. Thanks for joining us. See you 1582 01:22:43,360 --> 01:22:43,800 Speaker 4: next time. 1583 01:22:48,680 --> 01:22:51,920 Speaker 1: For more science and curiosity. Come find us on social media, 1584 01:22:52,000 --> 01:22:55,519 Speaker 1: where we answer questions and post videos. We're on Twitter, 1585 01:22:55,640 --> 01:22:59,280 Speaker 1: disc Org, Insta, and now TikTok. Thanks for listening. And 1586 01:22:59,320 --> 01:23:02,040 Speaker 1: remember that Daniel and Jorge Explain the Universe is a 1587 01:23:02,080 --> 01:23:06,680 Speaker 1: production of iHeartRadio. For more podcasts from iHeartRadio, visit the 1588 01:23:06,720 --> 01:23:10,880 Speaker 1: iHeartRadio app, Apple Podcasts, or wherever you listen to your 1589 01:23:10,960 --> 01:23:19,400 Speaker 1: favorite shows. When you pop a piece of cheese into 1590 01:23:19,400 --> 01:23:22,599 Speaker 1: your mouth, you're probably not thinking about the environmental impact. 1591 01:23:22,840 --> 01:23:25,400 Speaker 1: But the people in the dairy industry are. That's why 1592 01:23:25,479 --> 01:23:27,960 Speaker 1: they're working hard every day to find new ways to 1593 01:23:28,000 --> 01:23:32,360 Speaker 1: reduce waste, conserve natural resources, and drive down greenhouse gas emissions. 1594 01:23:32,640 --> 01:23:36,840 Speaker 1: House US dairy tackling greenhouse gases. Many farms use anaerobic 1595 01:23:36,880 --> 01:23:40,639 Speaker 1: digestors to turn the methane from manure into renewable energy 1596 01:23:40,680 --> 01:23:44,240 Speaker 1: that can power farms, towns, and electric cars. 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